Formularity: Software for Automated Formula Assignment of Natural and Other Organic Matter from Ultrahigh-Resolution Mass Spectra

Improved Characterization of Soil Organic Matter by Integrating FT-ICR MS, Liquid Chromatography Tandem Mass Spectrometry, and Molecular Networking: A Case Study of Root Litter Decay under Drought Conditions

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.

Selective Profiling of Carboxylic Acid in Crude Oil by Halogen Labeling Combined with Liquid Chromatography and High-Resolution Mass Spectrometry

Carboxylic acids are a small but essential compound class within petroleum chemistry, influencing crude oil behaviors in production and processing and causing environmental impacts. Detailed structural information is fundamental to understanding their influence on petroleum characteristics. However, characterizing acids in crude oil remains challenging due to matrix effects, structural diversity, and low abundance. In this work, we present a new methodology for profiling carboxylic acids by liquid-liquid extraction and selective derivatization using 4-bromo-N-methylbenzylamine (4-BNMA) followed by liquid chromatography and electrospray ionization Orbitrap mass spectrometry (LC-ESI-Orbitrap MS). The fragmentation of 4-BNMA derivatives produces a unique product ion pair, m/z 169/171, enabling the identification of chromatographic fractions containing carboxylic acids. The mass spectra of the corresponding fractions are extracted, and the acids are further computationally isolated based on the isotopic pattern. The method was optimized and validated using acid standards and systematic experimental designs, assuring robustness and sensitivity for nontarget screening purposes. This method detected up to 380 carboxylic acids in six Danish North Sea crude oils, with up to two carboxyl and other heteroatom functionalities (NSO). The results indicated that the most populated species are fatty acids (double bond equivalent (DBE) = 1) and small aromatic acids (DBE = 2-6). The predominance and diversities of compound classes in different samples are consistent with their corresponding bulk properties. Polyfunctional acids (Ox, NxOx, and SxOx) were observed due to exposure to oxidation and biodegradation. Also, the approach's applicability benefits high-resolution MS analysis by simplifying data processing for crude oil and potentially other high-organic and aqueous samples.

Detection and Exclusion of False-Positive Molecular Formula Assignments via Mass Error Distributions in UHR Mass Spectra of Natural Organic Matter

Ultrahigh resolution mass spectrometry (UHRMS) routinely detects and identifies thousands of mass peaks in complex mixtures, such as natural organic matter (NOM) and petroleum. The assignment of several chemically plausible molecular formulas (MFs) for a single accurate mass still poses a major problem for the reliable interpretation of NOM composition in a biogeochemical context. Applying sensible chemical rules for MF validation is often insufficient to eliminate multiple assignments (MultiAs)─especially for mass peaks with low abundance or if ample heteroatoms or isotopes are included - and requires manual inspection or expert judgment. Here, we present a new approach based on mass error distributions for the identification of true and false assignments among MultiAs. To this end, we used the mass error in millidalton (mDa), which was superior to the commonly used relative mass error in ppm. We developed an automatic workflow to group MultiAs based on their shared formula units and Kendrick mass defect values and to evaluate the mass error distribution. In this way, the number of valid assignments of chlorinated disinfection byproducts was increased by 8-fold as compared to only applying 37Cl/35Cl isotope ratio filters. Likewise, phosphorus-containing MFs can be differentiated against chlorine-containing MFs with high confidence. Further, false assignments of highly aromatic sulfur-containing MFs ("black sulfur") to sodium adducts in negative ionization mode can be excluded by applying our approach. Overall, MFs for mass peaks that are close to the detection limit or where naturally occurring isotopes are rare (e.g., 15N) or absent (e.g., P and F) can now be validated, substantially increasing the reliability of MF assignments and broadening the applicability of UHRMS analysis to even more complex samples and processes.

Wildfire Impacts on Molecular Changes of Dissolved Organic Matter during Its Passage through Soil

The frequency and intensity of global wildfires are escalating, leading to an increase in derived pyrogenic dissolved organic matter (pyDOM), which potentially influences the riverine carbon reservoir and poses risks to drinking water safety. However, changes in pyDOM properties as it traverses through soil to water bodies are highly understudied due to the challenges of simulating such processes under laboratory conditions. In this study, we extracted soil DOM along hillslope gradients and soil depths in both burned and unburned catchments post wildfire. Using high-resolution mass spectrometry and a substrate-explicit model, we observed significant increases in the relative abundance of condensed aromatics (ConAC) and tannins in wildfire-affected soil DOM. Wildfire-affected soil DOM also displayed a broader spectrum of molecular and thermodynamic properties, indicative of its diverse composition and reactivity. Furthermore, as the fire-induced weakening of topsoil microbial reprocessing abilities hindered the transformation of plant-derived DOM, the relative abundance of lignin-like compounds increased with soil depth in the fire regions. Meanwhile, the distribution of shared molecular formulas along the hillslope gradient (from shoulder to toeslope) exhibited analogous patterns in both burned and unburned catchments. Although there was an increased prevalence of ConAC and tannin in the burned catchments, the relative abundance of these fractions diminished along the hillslope in all three catchments. Based on the substrate-explicit model, the biodegradability exhibited by wildfire-affected DOM fractions offers the possibility of its conversion along hillslopes. Our findings reveal the spatial distribution of DOM properties after a wildfire, facilitating accurate evaluation of dissolved organic carbon composition involved in the watershed-scale carbon cycle.

Experimental Open Air Burning of Vegetation Enhances Organic Matter Chemical Heterogeneity Compared to Laboratory Burns

Wildfires produce solid residuals that have unique chemical and physical properties compared to unburned materials, which influence their cycling and fate in the natural environment. Visual burn severity assessment is used to evaluate post-fire alterations to the landscape in field-based studies, yet muffle furnace methods are commonly used in laboratory studies to assess molecular scale alterations along a temperature continuum. Here, we examined solid and leachable organic matter characteristics from chars visually characterized as low burn severity that were created either on an open air burn table or from low-temperature muffle furnace burns. We assessed how the different combustion conditions influence solid and dissolved organic matter chemistries and explored the potential influence of these results on the environmental fate and reactivity. Notably, muffle furnace chars produced less leachable carbon and nitrogen than open air chars across land cover types. Organic matter produced from muffle furnace burns was more homogeneous than open air chars. This work highlights chemical heterogeneities that exist within a single burn severity category, potentially influencing our conceptual understanding of pyrogenic organic matter cycling in the natural environment, including transport and processing in watersheds. Therefore, we suggest that open air burn studies are needed to further advance our understanding of pyrogenic organic matter's environmental reactivity and fate.

Microbial polyphenol metabolism is part of the thawing permafrost carbon cycle

With rising global temperatures, permafrost carbon stores are vulnerable to microbial degradation. The enzyme latch theory states that polyphenols should accumulate in saturated peatlands due to diminished phenol oxidase activity, inhibiting resident microbes and promoting carbon stabilization. Pairing microbiome and geochemical measurements along a permafrost thaw-induced saturation gradient in Stordalen Mire, a model Arctic peatland, we confirmed a negative relationship between phenol oxidase expression and saturation but failed to support other trends predicted by the enzyme latch. To inventory alternative polyphenol removal strategies, we built CAMPER, a gene annotation tool leveraging polyphenol enzyme knowledge gleaned across microbial ecosystems. Applying CAMPER to genome-resolved metatranscriptomes, we identified genes for diverse polyphenol-active enzymes expressed by various microbial lineages under a range of redox conditions. This shifts the paradigm that polyphenols stabilize carbon in saturated soils and highlights the need to consider both oxic and anoxic polyphenol metabolisms to understand carbon cycling in changing ecosystems.

Utilising Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to track the oxidation of lignin by an alkaliphilic laccase

Lignin is a complex heteroaromatic polymer which is one of the most abundant and diverse biopolymers on the planet. It comprises approximately one third of all woody plant matter, making it an attractive candidate as an alternative, renewable feedstock to petrochemicals to produce fine chemicals. However, the inherent complexity of lignin makes it difficult to analyse and characterise using common analytical techniques, proving a hindrance to the utilisation of lignin as a green chemical feedstock. Herein we outline the tracking of lignin degradation by an alkaliphilic laccase in a semi-quantitative manner using a combined chemical analysis approach using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to characterise shifts in chemical diversity and relative abundance of ions, and NMR to highlight changes in the structure of lignin. Specifically, an alkaliphilic laccase was used to degrade an industrially relevant lignin, with compounds such as syringaresinol being almost wholly removed (95%) after 24 hours of treatment. Structural analyses reinforced these findings, indicating a >50% loss of NMR signal relating to β-β linkages, of which syringaresinol is representative. Ultimately, this work underlines a combined analytical approach that can be used to gain a broader semi-quantitative understanding of the enzymatic activity of laccases within a complex, non-model mixture.

Noise Filtering Algorithm Using Gaussian Mixture Models for High-Resolution Mass Spectra of Natural Organic Matter

High-resolution mass spectra of natural organic matter (NOM) contain a large number of noise signals. These signals interfere with the correct molecular composition estimation during nontargeted analysis because formula-assignment programs find empirical formulas for such peaks as well. Previously proposed noise filtering methods that utilize the profile of the intensity distribution of mass spectrum peaks rely on a histogram to calculate the intensity threshold value. However, the histogram profile can vary depending on the user settings. In addition, these algorithms are not automated, so they are handled manually. To overcome the mentioned drawbacks, we propose a new algorithm for noise filtering in mass spectra. This filter is based on Gaussian Mixture Models (GMMs), a machine learning method to find the intensity threshold value. The algorithm is completely data-driven and eliminates the need to work with a histogram. It has no customizable parameters and automatically determines the noise level for each individual mass spectrum. The algorithm performance was tested on mass spectra of natural organic matter obtained by averaging a different number of microscans (transients), and the results were compared with other noise filters proposed in the literature. Finally, the effect of this noise filtering approach on the fraction of peaks with assigned formulas was investigated. It was shown that there is always an increase in the identification rate, but the magnitude of the effect changes with the number of microscans averaged. The increase can be as high as 15%.

Graph-Based Method for Calibration of High-Resolution Mass Spectra of Natural Organic Matter

Inaccuracies in ion detection and signal processing can undermine confidence in the molecular formula assignment of high-resolution mass spectrometry, which relies on precise matching of the mass-to-charge ratio (m/z). This study proposes a novel graph-based spectra calibration method, MSCMcalib, which implements coordinate transformation and pattern detection. MSCMcalib maps uncalibrated m/z data onto a modified 2D mass defect plot, facilitating the automatic calibration of detected lines, i.e., the calibration of uncalibrated peaks aligned with these lines. The "propagation" method is subsequently employed to accurately and automatically calibrate 605 m/z values across multiple lines, encompassing 98% of the m/z range. The calibrated m/z values divide the m/z range of the spectrum into multiple subintervals, with each subinterval undergoing a process of "scaling" calibration. The utilization of narrower partitions effectively mitigates divergence issues at both ends that arise from the polynomial fitting of errors against m/z. The effectiveness of MSCMcalib is validated through the calibration of SRFA data with m/z error ranges spanning from -10 to -6 ppm, resulting in an additional assignment of 11%-30% more molecular formulas compared to the quadratic fitting calibration.

Chemodiversity of riverine dissolved organic matter: Effects of local environments and watershed characteristics

Riverine dissolved organic matter (DOM) is crucial to global carbon cycling and aquatic ecosystems. However, the geographical patterns and environmental drivers of DOM chemodiversity remain elusive especially in the waters and sediments of continental rivers. Here, we systematically analyzed DOM molecular diversity and composition in surface waters and sediments across 97 broadly distributed rivers using data from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium. We further examined the associations of molecular richness and composition with geographical, climatic, physicochemical variables, as well as the watershed characteristics. We found that molecular richness significantly decreased toward higher latitudes, but only in sediments (r = -0.24, p < 0.001). The environmental variables like precipitation and non-purgeable organic carbon showed strong associations with DOM molecular richness and composition. Interestingly, we identified that less-documented factors like watershed characteristics were also related to DOM molecular richness and composition. For instance, DOM molecular richness was positively correlated with the soil sand fraction for waters, while with the percentage of forest for sediments. Importantly, the effects of watershed characteristics on DOM molecular richness and composition were generally stronger in waters than sediments. This phenomenon was further supported by the fact that 11 out of 13 watershed characteristics (e.g., the percentages of impervious area and cropland) showed more positive than negative correlations with molecular abundance especially in waters. As the percentage of forest increased, there was a continuous accumulation of the compounds with higher molecular weight, aromaticity, and degree of unsaturation. In contrast, human activities accumulated the compounds with lower molecular weight and oxygenation, and higher bioavailability. Our findings imply that it may be possible to use a small set of broadly available data types to predict DOM molecular richness and composition across diverse river systems. Elucidation of mechanisms underlying these relationships will provide further enhancements to such predictions, especially when extrapolating to unsampled systems.

Experimental warming accelerates positive soil priming in a temperate grassland ecosystem

Unravelling biosphere feedback mechanisms is crucial for predicting the impacts of global warming. Soil priming, an effect of fresh plant-derived carbon (C) on native soil organic carbon (SOC) decomposition, is a key feedback mechanism that could release large amounts of soil C into the atmosphere. However, the impacts of climate warming on soil priming remain elusive. Here, we show that experimental warming accelerates soil priming by 12.7% in a temperate grassland. Warming alters bacterial communities, with 38% of unique active phylotypes detected under warming. The functional genes essential for soil C decomposition are also stimulated, which could be linked to priming effects. We incorporate lab-derived information into an ecosystem model showing that model parameter uncertainty can be reduced by 32-37%. Model simulations from 2010 to 2016 indicate an increase in soil C decomposition under warming, with a 9.1% rise in priming-induced CO2 emissions. If our findings can be generalized to other ecosystems over an extended period of time, soil priming could play an important role in terrestrial C cycle feedbacks and climate change.

Internal exposure potential of water-soluble organic molecules in urban PM2.5 evaluated by non-covalent adductome of human serum albumin

Water-soluble organic molecules (WSOMs) in inhaled PM2.5 can readily translocate from the lungs into the blood circulation, facilitating their distribution to and health effects on distant organs and tissues in the human body. Human serum albumin (HSA), the most abundant protein carrier in the blood, readily binds exogenous substances to form non-covalent adducts and subsequently transports them throughout the circulatory system, thereby indicating their internal exposure. The direct internal exposure of WSOMs in PM2.5 needs to be understood. In this study, the non-covalent HSA-WSOM adductome was developed as a dosimeter to evaluate the internal exposure potential of WSOMs in urban PM2.5. The WSOM composition was acquired from non-target high-resolution mass spectrometry analysis coupled with multiple ionizations. The binding level of HSA-WSOM non-covalent adducts was obtained from surface plasma resonance. Machine learning combined WSOM composition and the binding level of HSA-WSOM non-covalent adducts to screen bindable (also internalizable) WSOMs. The concentration of WSOM ranged from 4 to 13 μg/m3 during our observation period. Of the 17,513 mass spectral features detected, 9,484 contributed to the non-covalent adductome and possessed the internal exposure potential. 102 major contributors accounted for 90.6 % of the HSA-WSOM binding level. The fraction of internalizable WSOMs in PM2.5 varied from 11.9 % to 61.3 %, averaging 26.2 %. WSOMs that have internal exposure potential were primarily lignin-like and lipid-like substances. The HSA-WSOMs non-covalent adductome represents direct internal exposure potential, which can provide crucial insights into the molecular diagnosis of PM2.5 exposure and precise assessments of PM2.5 health effects.

Thermal responses of dissolved organic matter under global change

The diversity of intrinsic traits of different organic matter molecules makes it challenging to predict how they, and therefore the global carbon cycle, will respond to climate change. Here we develop an indicator of compositional-level environmental response for dissolved organic matter to quantify the aggregated response of individual molecules that positively and negatively associate with warming. We apply the indicator to assess the thermal response of sediment dissolved organic matter in 480 aquatic microcosms along nutrient gradients on three Eurasian mountainsides. Organic molecules consistently respond to temperature change within and across contrasting climate zones. At a compositional level, dissolved organic matter in warmer sites has a stronger thermal response and shows functional reorganization towards molecules with lower thermodynamic favorability for microbial decomposition. The thermal response is more sensitive to warming at higher nutrients, with increased sensitivity of up to 22% for each additional 1 mg L-1 of nitrogen loading. The utility of the thermal response indicator is further confirmed by laboratory experiments and reveals its positive links to greenhouse gas emissions.

Environment-specific virocell metabolic reprogramming

Viruses impact microbial systems through killing hosts, horizontal gene transfer, and altering cellular metabolism, consequently impacting nutrient cycles. A virus-infected cell, a "virocell," is distinct from its uninfected sister cell as the virus commandeers cellular machinery to produce viruses rather than replicate cells. Problematically, virocell responses to the nutrient-limited conditions that abound in nature are poorly understood. Here we used a systems biology approach to investigate virocell metabolic reprogramming under nutrient limitation. Using transcriptomics, proteomics, lipidomics, and endo- and exo-metabolomics, we assessed how low phosphate (low-P) conditions impacted virocells of a marine Pseudoalteromonas host when independently infected by two unrelated phages (HP1 and HS2). With the combined stresses of infection and nutrient limitation, a set of nested responses were observed. First, low-P imposed common cellular responses on all cells (virocells and uninfected cells), including activating the canonical P-stress response, and decreasing transcription, translation, and extracellular organic matter consumption. Second, low-P imposed infection-specific responses (for both virocells), including enhancing nitrogen assimilation and fatty acid degradation, and decreasing extracellular lipid relative abundance. Third, low-P suggested virocell-specific strategies. Specifically, HS2-virocells regulated gene expression by increasing transcription and ribosomal protein production, whereas HP1-virocells accumulated host proteins, decreased extracellular peptide relative abundance, and invested in broader energy and resource acquisition. These results suggest that although environmental conditions shape metabolism in common ways regardless of infection, virocell-specific strategies exist to support viral replication during nutrient limitation, and a framework now exists for identifying metabolic strategies of nutrient-limited virocells in nature.

Biogeochemistry of upland to wetland soils, sediments, and surface waters across Mid-Atlantic and Great Lakes coastal interfaces

Transferable and mechanistic understanding of cross-scale interactions is necessary to predict how coastal systems respond to global change. Cohesive datasets across geographically distributed sites can be used to examine how transferable a mechanistic understanding of coastal ecosystem control points is. To address the above research objectives, data were collected by the EXploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments (EXCHANGE) Consortium - a regionally distributed network of researchers that collaborated on experimental design, methodology, collection, analysis, and publication. The EXCHANGE Consortium collected samples from 52 coastal terrestrial-aquatic interfaces (TAIs) during Fall of 2021. At each TAI, samples collected include soils from across a transverse elevation gradient (i.e., coastal upland forest, transitional forest, and wetland soils), surface waters, and nearshore sediments across research sites in the Great Lakes and Mid-Atlantic regions (Chesapeake and Delaware Bays) of the continental USA. The first campaign measures surface water quality parameters, bulk geochemical parameters on water, soil, and sediment samples, and physicochemical parameters of sediment and soil.

Uncovering the dominant role of root metabolism in shaping rhizosphere metabolome under drought in tropical rainforest plants

Plant-soil-microbe interactions are crucial for driving rhizosphere processes that contribute to metabolite turnover and nutrient cycling. With the increasing frequency and severity of water scarcity due to climate warming, understanding how plant-mediated processes, such as root exudation, influence soil organic matter turnover in the rhizosphere is essential. In this study, we used 16S rRNA gene amplicon sequencing, rhizosphere metabolomics, and position-specific 13C-pyruvate labeling to examine the effects of three different plant species (Piper auritum, Hibiscus rosa sinensis, and Clitoria fairchildiana) and their associated microbial communities on soil organic carbon turnover in the rhizosphere. Our findings indicate that in these tropical plants, the rhizosphere metabolome is primarily shaped by the response of roots to drought rather than direct shifts in the rhizosphere bacterial community composition. Specifically, the reduced exudation of plant roots had a notable effect on the metabolome of the rhizosphere of P. auritum, with less reliance on neighboring microbes. Contrary to P. auritum, H. rosa sinensis and C. fairchildiana experienced changes in their exudate composition during drought, causing alterations to the bacterial communities in the rhizosphere. This, in turn, had a collective impact on the rhizosphere's metabolome. Furthermore, the exclusion of phylogenetically distant microbes from the rhizosphere led to shifts in its metabolome. Additionally, C. fairchildiana appeared to be associated with only a subset of symbiotic bacteria under drought conditions. These results indicate that plant species-specific microbial interactions systematically change with the root metabolome. As roots respond to drought, their associated microbial communities adapt, potentially reinforcing the drought tolerance strategies of plant roots. These findings have significant implications for maintaining plant health and preference during drought stress and improving plant performance under climate change.

Determining the parent and associated fragment formulae in mass spectrometry via the parent subformula graph

BACKGROUND

Identifying the molecular formula and fragmentation reactions of an unknown compound from its mass spectrum is crucial in areas such as natural product chemistry and metabolomics. We propose a method for identifying the correct candidate formula of an unidentified natural product from its mass spectrum. The method involves scoring the plausibility of parent candidate formulae based on a parent subformula graph (PSG), and two possible metrics relating to the number of edges in the PSG. This method is applicable to both electron-impact mass spectrometry (EI-MS) and tandem mass spectrometry (MS/MS) data. Additionally, this work introduces the two-dimensional fragmentation plot (2DFP) for visualizing PSGs.

RESULTS

Our results suggest that incorporating information regarding the edges of the PSG results in enhanced performance in correctly identifying parent formulae, in comparison to the more well-accepted "MS/MS score", on the 2016 Computational Assessment of Small Molecule Identification (CASMI 2016) data set (76.3 vs 58.9% correct formula identification) and the Research Centre for Toxic Compounds in the Environment (RECETOX) data set (66.2% vs 59.4% correct formula identification). In the extension of our method to identify the correct candidate formula from complex EI-MS data of semiochemicals, our method again performed better (correct formula appearing in the top 4 candidates in 20/23 vs 7/23 cases) than the MS/MS score, and enables the rapid identification of both the correct parent ion mass and the correct parent formula with minimal expert intervention.

CONCLUSION

Our method reliably identifies the correct parent formula even when the mass information is ambiguous. Furthermore, should parent formula identification be successful, the majority of associated fragment formulae can also be correctly identified. Our method can also identify the parent ion and its associated fragments in EI-MS spectra where the identity of the parent ion is unclear due to low quantities and overlapping compounds. Finally, our method does not inherently require empirical fitting of parameters or statistical learning, meaning it is easy to implement and extend upon.

SCIENTIFIC CONTRIBUTION

Developed, implemented and tested new metrics for assessing plausibility of candidate molecular formulae obtained from HR-MS data.

Characterization of halogenated organic compounds by the Fourier transform ion cyclotron resonance mass spectrometry: A critical review

Halogenated organic compounds (HOCs), widely present in various environments, are generally formed by natural processes (e.g., photochemical halogenation) and anthropogenic activities (e.g., water disinfection and anthropogenic discharge of HOCs), posing health and environmental risks. Therefore, in-depth knowledge of the molecular composition, transformation, and fate of HOCs is crucial to regulate and reduce their formation. Because of the extremely complex nature of HOCs and their precursors, the molecular composition of HOCs remains largely unknown. The Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) offers the most powerful resolution and mass accuracy for the simultaneous molecular-level characterization of HOCs and their precursors. However, there is still a paucity of reviews regarding the comprehensive characterization of HOCs by FT-ICR MS. Based on the FT-ICR MS, the formation mechanism, sample pretreatment, and analysis methods were summarized for two typical HOCs classes, namely halogenated disinfection byproducts and per- and polyfluoroalkyl substances in this review. Moreover, we have highlighted data analysis methods and some typical applications of HOCs using FT-ICR MS and proposed suggestions for current issues. This review will deepen our understanding of the chemical characterization of HOCs and their formation mechanisms and transformation at the molecular level in aquatic systems, facilitating the application of the state-of-the-art FT-ICR MS in environmental and geochemical research.

Quantifying Stochastic Processes in Shaping Dissolved Organic Matter Pool with High-Resolution Mass Spectrometry

Natural dissolved organic matter (DOM) represents a ubiquitous molecular mixture, progressively characterized by spatiotemporal resolution. However, an inadequate comprehension of DOM molecular dynamics, especially the stochastic processes involved, hinders carbon cycling predictions. This study employs ecological principles to introduce a neutral theory to elucidate the fundamental processes involving molecular generation, degradation, and migration. A neutral model is thus formulated to assess the probability distribution of DOM molecules, whose frequencies and abundances follow a β-distribution relationship. The neutral model is subsequently validated with high-resolution mass spectrometry (HRMS) data from various waterbodies, including lakes, rivers, and seas. The model fitting highlights the prevalence of molecular neutral distribution and quantifies the stochasticity within DOM molecular dynamics. Furthermore, the model identifies deviations of HRMS observations from neutral expectations in photochemical and microbial experiments, revealing nonrandom molecular transformations. The ecological null model further validates the neutral modeling results, demonstrating that photodegradation reduces molecular stochastic dynamics at the surface of an acidic pit lake, while random distribution intensifies at the river surface compared with the porewater. Taken together, the DOM molecular neutral model emphasizes the significance of stochastic processes in shaping a natural DOM pool, offering a potential theoretical framework for DOM molecular dynamics in aquatic and other ecosystems.

TEnvR: MATLAB-based toolbox for environmental research

With the advancements in science and technology, datasets become larger and more multivariate, which warrants the need for programming tools for fast data processing and multivariate statistical analysis. Here, the MATLAB-based Toolbox for Environmental Research "TEnvR" (pronounced "ten-ver") is introduced. This novel toolbox includes 44 open-source codes for automated data analysis from a multitude of techniques, such as ultraviolet-visible, fluorescence, and nuclear magnetic resonance spectroscopies, as well as from ultrahigh resolution mass spectrometry. Provided are codes for processing data (e.g., spectral corrections, formula assignment), visualization of figures, calculation of metrics, multivariate statistics, and automated work-up of large datasets. TEnvR allows for efficient data analysis with minimal "by-hand" manual work by the user, which allows scientists to do research more efficiently. This manuscript is supplemented with a detailed tutorial, example data, and screenshots, which collectively provide instructions on how to use all codes. TEnvR is novice-friendly and experience in programming with MATLAB is not required. TEnvR fulfills the need for a concise MATLAB-based toolbox for working with environmental data and will be updated annually to keep pace with the latest advances and needs for computational work in the environmental sciences.

Rethinking Aerobic Respiration in the Hyporheic Zone under Variation in Carbon and Nitrogen Stoichiometry

Hyporheic zones (HZs)─zones of groundwater-surface water mixing─are hotspots for dissolved organic matter (DOM) and nutrient cycling that can disproportionately impact aquatic ecosystem functions. However, the mechanisms affecting DOM metabolism through space and time in HZs remain poorly understood. To resolve this gap, we investigate a recently proposed theory describing trade-offs between carbon (C) and nitrogen (N) limitations as a key regulator of HZ metabolism. We propose that throughout the extent of the HZ, a single process like aerobic respiration (AR) can be limited by both DOM thermodynamics and N content due to highly variable C/N ratios over short distances (centimeter scale). To investigate this theory, we used a large flume, continuous optode measurements of dissolved oxygen (DO), and spatially and temporally resolved molecular analysis of DOM. Carbon and N limitations were inferred from changes in the elemental stoichiometric ratio. We show sequential, depth-stratified relationships of DO with DOM thermodynamics and organic N that change across centimeter scales. In the shallow HZ with low C/N, DO was associated with the thermodynamics of DOM, while deeper in the HZ with higher C/N, DO was associated with inferred biochemical reactions involving organic N. Collectively, our results suggest that there are multiple competing processes that limit AR in the HZ. Resolving this spatiotemporal variation could improve predictions from mechanistic models, either via more highly resolved grid cells or by representing AR colimitation by DOM thermodynamics and organic N.

Progressive drought alters the root exudate metabolome and differentially activates metabolic pathways in cotton (Gossypium hirsutum)

Root exudates comprise various primary and secondary metabolites that are responsive to plant stressors, including drought. As increasing drought episodes are predicted with climate change, identifying shifts in the metabolome profile of drought-induced root exudation is necessary to understand the molecular interactions that govern the relationships between plants, microbiomes, and the environment, which will ultimately aid in developing strategies for sustainable agriculture management. This study utilized an aeroponic system to simulate progressive drought and recovery while non-destructively collecting cotton (Gossypium hirsutum) root exudates. The molecular composition of the collected root exudates was characterized by untargeted metabolomics using Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) and mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Over 700 unique drought-induced metabolites were identified throughout the water-deficit phase. Potential KEGG pathways and KEGG modules associated with the biosynthesis of flavonoid compounds, plant hormones (abscisic acid and jasmonic acid), and other secondary metabolites were highly induced under severe drought, but not at the wilting point. Additionally, the associated precursors of these metabolites, such as amino acids (phenylalanine and tyrosine), phenylpropanoids, and carotenoids, were also mapped. The potential biochemical transformations were further calculated using the data generated by FT-ICR MS. Under severe drought stress, the highest number of potential biochemical transformations, including methylation, ethyl addition, and oxidation/hydroxylation, were identified, many of which are known reactions in some of the mapped pathways. With the application of FT-ICR MS, we revealed the dynamics of drought-induced secondary metabolites in root exudates in response to drought, providing valuable information for drought-tolerance strategies in cotton.

Spatial and temporal metagenomics of river compartments reveals viral community dynamics in an urban impacted stream

Although river ecosystems comprise less than 1% of Earth's total non-glaciated area, they are critical modulators of microbially and virally orchestrated global biogeochemical cycles. However, most studies either use data that is not spatially resolved or is collected at timepoints that do not reflect the short life cycles of microorganisms. As a result, the relevance of microbiome interactions and the impacts they have over time on biogeochemical cycles are poorly understood. To assess how viral and microbial communities change over time, we sampled surface water and pore water compartments of the wastewater-impacted River Erpe in Germany every 3 hours over a 48-hour period resulting in 32 metagenomes paired to geochemical and metabolite measurements. We reconstructed 6,500 viral and 1,033 microbial genomes and found distinct communities associated with each river compartment. We show that 17% of our vMAGs clustered to viruses from other ecosystems like wastewater treatment plants and rivers. Our results also indicated that 70% of the viral community was persistent in surface waters, whereas only 13% were persistent in the pore waters taken from the hyporheic zone. Finally, we predicted linkages between 73 viral genomes and 38 microbial genomes. These putatively linked hosts included members of the Competibacteraceae, which we suggest are potential contributors to carbon and nitrogen cycling. Together, these findings demonstrate that microbial and viral communities in surface waters of this urban river can exist as stable communities along a flowing river; and raise important considerations for ecosystem models attempting to constrain dynamics of river biogeochemical cycles.

Microbial sensitivity to temperature and sulfate deposition modulates greenhouse gas emissions from peat soils

Peatlands are among the largest natural sources of atmospheric methane (CH₄ ) worldwide. Microbial processes play a key role in regulating CH₄ emissions from peatland ecosystems, yet the complex interplay between soil substrates and microbial communities in controlling CH₄ emissions as a function of global change remains unclear. Herein, we performed an integrated analysis of multi-omics data sets to provide a comprehensive understanding of the molecular processes driving changes in greenhouse gas (GHG) emissions in peatland ecosystems with increasing temperature and sulfate deposition in a laboratory incubation study. We sought to first investigate how increasing temperatures (4, 21, and 35°C) impact soil microbiome-metabolome interactions; then explore the competition between methanogens and sulfate-reducing bacteria (SRBs) with increasing sulfate concentrations at the optimum temperature for methanogenesis. Our results revealed that peat soil organic matter degradation, mediated by biotic and potentially abiotic processes, is the main driver of the increase in CO₂ production with temperature. In contrast, the decrease in CH₄ production at 35°C was linked to the absence of syntrophic communities and the potential inhibitory effect of phenols on methanogens. Elevated temperatures further induced the microbial communities to develop high growth yield and stress tolerator trait-based strategies leading to a shift in their composition and function. On the other hand, SRBs were able to outcompete methanogens in the presence of non-limiting sulfate concentrations at 21°C, thereby reducing CH₄ emissions. At higher sulfate concentrations, however, the prevalence of communities capable of producing sufficient low-molecular-weight carbon substrates for the coexistence of SRBs and methanogens was translated into elevated CH₄ emissions. The use of omics in this study enhanced our understanding of the structure and interactions among microbes with the abiotic components of the system that can be useful for mitigating GHG emissions from peatland ecosystems in the face of global change.

Complementary Elucidation of the Molecular Characteristics of Groundwater Dissolved Organic Matter Using Ultrahigh-Resolution Mass Spectrometry Coupled with Negative- and Positive-Ion Electrospray Ionization

The formula assignment of the Fourier transform ion cyclotron resonance mass spectrometry coupled with positive-ion electrospray ionization [ESI(+)-FT-ICR MS] is challenging because of the extensive occurrence of adducts. However, there is a paucity of automated formula assignment methods for ESI(+)-FT-ICR MS spectra. The novel automated formula assignment algorithm for ESI(+)-FT-ICR MS spectra developed herein has been applied to elucidate the composition of dissolved organic matter (DOM) in groundwater during air-induced ferrous [Fe(II)] oxidation. The ESI(+)-FT-ICR MS spectra of groundwater DOM were profoundly impacted by [M + Na]⁺ adducts and, to a lesser extent, [M + K]⁺ adducts. Oxygen-poor and N-containing compounds were frequently detected when the FT-ICR MS was operated in the ESI(+) mode, while the components with higher carbon oxidation states were preferentially ionized in the negative-ion electrospray ionization [ESI(-)] mode. Values for the difference between double-bond equivalents and the number of oxygen atoms from -13 to 13 are proposed for the formula assignment of the ESI(+)-FT-ICR MS spectra of aquatic DOM. Furthermore, for the first time, the Fe(II)-mediated formation of highly toxic organic iodine species was reported in groundwater rich in Fe(II), iodide, and DOM. The results of this study not only shed light on the further algorithm development for comprehensive characterization of DOM by ESI(-)-FT-ICR MS and ESI(+)-FT-ICR MS but also highlight the importance of appropriate treatment of specific groundwater prior to use.

Machine-learning assisted molecular formula assignment to high-resolution mass spectrometry data of dissolved organic matter

High-resolution mass spectrometry (HRMS) provides molecular compositional information of dissolved organic matter (DOM) through isotopic assignment from the molecular mass. However, due to the inevitable deviation of molecular mass measurement and the limitation of resolving power, multiple possible solutions frequently occur for a given molecular mass. Lowering the mass deviation threshold and adding assignment restriction rules are often applied to exclude the incorrect solutions, which generally involves time-consuming manual post-processing of mass data. To improve the result accuracy in an automated manner, we developed a molecular formula assignment algorithm based on machine-learning technology. The method integrated a logistic regression model using manually corrected isotopic composition and the peak features of HRMS data (m/z, signal-to-noise ratio, isotope type, and number, etc.) as training data. The developed model can evaluate the correctness of a candidate formula for the given mass peak based on the peak features. The method was verified by various DOM samples FT-ICR MS data (direct infusion negative mode electrospray), achieving a ∼90% accuracy (compared to the traditional approach) for formula assignment. The method was applied to a series of NOM samples and showed a significant improvement in formula assignment compared with the mass matching method.

MetaboDirect: an analytical pipeline for the processing of FT-ICR MS-based metabolomic data

BACKGROUND

Microbiomes are now recognized as the main drivers of ecosystem function ranging from the oceans and soils to humans and bioreactors. However, a grand challenge in microbiome science is to characterize and quantify the chemical currencies of organic matter (i.e., metabolites) that microbes respond to and alter. Critical to this has been the development of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), which has drastically increased molecular characterization of complex organic matter samples, but challenges users with hundreds of millions of data points where readily available, user-friendly, and customizable software tools are lacking.

RESULTS

Here, we build on years of analytical experience with diverse sample types to develop MetaboDirect, an open-source, command-line-based pipeline for the analysis (e.g., chemodiversity analysis, multivariate statistics), visualization (e.g., Van Krevelen diagrams, elemental and molecular class composition plots), and presentation of direct injection high-resolution FT-ICR MS data sets after molecular formula assignment has been performed. When compared to other available FT-ICR MS software, MetaboDirect is superior in that it requires a single line of code to launch a fully automated framework for the generation and visualization of a wide range of plots, with minimal coding experience required. Among the tools evaluated, MetaboDirect is also uniquely able to automatically generate biochemical transformation networks (ab initio) based on mass differences (mass difference network-based approach) that provide an experimental assessment of metabolite connections within a given sample or a complex metabolic system, thereby providing important information about the nature of the samples and the set of microbial reactions or pathways that gave rise to them. Finally, for more experienced users, MetaboDirect allows users to customize plots, outputs, and analyses.

CONCLUSION

Application of MetaboDirect to FT-ICR MS-based metabolomic data sets from a marine phage-bacterial infection experiment and a Sphagnum leachate microbiome incubation experiment showcase the exploration capabilities of the pipeline that will enable the research community to evaluate and interpret their data in greater depth and in less time. It will further advance our knowledge of how microbial communities influence and are influenced by the chemical makeup of the surrounding system. The source code and User's guide of MetaboDirect are freely available through ( https://github.com/Coayala/MetaboDirect ) and ( https://metabodirect.readthedocs.io/en/latest/ ), respectively. Video Abstract.

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.

Joint role of land cover types and microbial processing on molecular composition of dissolved organic matter in inland lakes

Anthropogenic activities have greatly changed the land use and land cover (LULC) and further influenced the chemical properties and amount of DOM transported into aquatic systems, meanwhile, microbial processing is also critical to DOM molecular composition in freshwaters. However, how they jointly shape DOM's chemical composition and chemodiversity in lakes is poorly understood. Here we examined DOM characteristics for seven inland lakes with three different land cover conditions (forest-dominated, cropland-dominated, and urban-dominated). Results indicated that DOM in cropland-dominated and forest-dominated lakes exhibited more characteristics of terrestrial organic matter, while urban-dominated lakes had more allochthonous organic matter driven by relatively high nutrient input. Human activities extended terrestrial DOM input to lakes and intensified the amount of heteroatomic organic molecules containing nitrogen and sulfur in lakes, with cropland contributing more N-containing compounds and urban contributing more S-containing compounds. Differential bacterial community composition appeared in the three types of land cover lakes, while strong co-occurrence/exclusion patterns between specific microbes and molecular formula groups revealed the key DOM metabolism functions of these bacteria. Matrix correlations based on Mantel tests confirmed that watershed landcover status was a dominating factor for DOM sources and molecular composition in mountainous lakes through direct input of terrestrial organic matter, and microbial processing was not the key factor for DOM molecular formula. Our findings help to assess the influence of human activities and microbial processing in the transfer and transformation of DOM in environmental waters.

Dark Matter Enhances Interactions within Both Microbes and Dissolved Organic Matter under Global Change

There are vast but uncharacterized microbial taxa and chemical metabolites (that is, dark matter) across the Earth's ecosystems. A lack of knowledge about dark matter hinders a complete understanding of microbial ecology and biogeochemical cycles. Here, we examine sediment bacteria and dissolved organic matter (DOM) in 300 microcosms along experimental global change gradients in subtropical and subarctic climate zones of China and Norway, respectively. We develop an indicator to quantify the importance of dark matter by comparing co-occurrence network patterns with and without dark matter in bacterial or DOM assemblages. In both climate zones, dark matter constitutes approximately 30-56% of bacterial taxa and DOM metabolites and changes connectivity within bacterial and DOM assemblages by between -15.5 and +61.8%. Dark matter is generally more important for changing network connectivity within DOM assemblages than those of microbes, especially in the subtropical zone. However, the importance of dark matter along global change gradients is strongly correlated between bacteria and DOM and consistently increased toward higher primary productivity because of increasing temperatures and nutrient enrichment. Our findings highlight the importance of microbial and chemical dark matter for changing biogeochemical interactions under global change.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

Root exudate composition reflects drought severity gradient in blue grama (Bouteloua gracilis)

Plant survival during environmental stress greatly affects ecosystem carbon (C) cycling, and plant–microbe interactions are central to plant stress survival. The release of C-rich root exudates is a key mechanism plants use to manage their microbiome, attracting beneficial microbes and/or suppressing harmful microbes to help plants withstand environmental stress. However, a critical knowledge gap is how plants alter root exudate concentration and composition under varying stress levels. In a greenhouse study, we imposed three drought treatments (control, mild, severe) on blue grama (Bouteloua gracilis Kunth Lag. Ex Griffiths), and measured plant physiology and root exudate concentration and composition using GC–MS, NMR, and FTICR. With increasing drought severity, root exudate total C and organic C increased concurrently with declining predawn leaf water potential and photosynthesis. Root exudate composition mirrored the physiological gradient of drought severity treatments. Specific compounds that are known to alter plant drought responses and the rhizosphere microbiome mirrored the drought severity-induced root exudate compositional gradient. Despite reducing C uptake, these plants actively invested C to root exudates with increasing drought severity. Patterns of plant physiology and root exudate concentration and composition co-varied along a gradient of drought severity.

Using Macro- and Microscale Preservation in Vertebrate Fossils as Predictors for Molecular Preservation in Fluvial Environments

Exceptionally preserved fossils retain soft tissues and often the biomolecules that were present in an animal during its life. The majority of terrestrial vertebrate fossils are not traditionally considered exceptionally preserved, with fossils falling on a spectrum ranging from very well-preserved to poorly preserved when considering completeness, morphology and the presence of microstructures. Within this variability of anatomical preservation, high-quality macro-scale preservation (e.g., articulated skeletons) may not be reflected in molecular-scale preservation (i.e., biomolecules). Excavation of the Hayden Quarry (HQ; Chinle Formation, Ghost Ranch, NM, USA) has resulted in the recovery of thousands of fossilized vertebrate specimens. This has contributed greatly to our knowledge of early dinosaur evolution and paleoenvironmental conditions during the Late Triassic Period (~212 Ma). The number of specimens, completeness of skeletons and fidelity of osteohistological microstructures preserved in the bone all demonstrate the remarkable quality of the fossils preserved at this locality. Because the Hayden Quarry is an excellent example of good preservation in a fluvial environment, we have tested different fossil types (i.e., bone, tooth, coprolite) to examine the molecular preservation and overall taphonomy of the HQ to determine how different scales of preservation vary within a single locality. We used multiple high-resolution mass spectrometry techniques (TOF-SIMS, GC-MS, FT-ICR MS) to compare the fossils to unaltered bone from extant vertebrates, experimentally matured bone, and younger dinosaurian skeletal material from other fluvial environments. FT-ICR MS provides detailed molecular information about complex mixtures, and TOF-SIMS has high elemental spatial sensitivity. Using these techniques, we did not find convincing evidence of a molecular signal that can be confidently interpreted as endogenous, indicating that very good macro- and microscale preservation are not necessarily good predictors of molecular preservation.

Wildfire-dependent changes in soil microbiome diversity and function

Forest soil microbiomes have crucial roles in carbon storage, biogeochemical cycling and rhizosphere processes. Wildfire season length, and the frequency and size of severe fires have increased owing to climate change. Fires affect ecosystem recovery and modify soil microbiomes and microbially mediated biogeochemical processes. To study wildfire-dependent changes in soil microbiomes, we characterized functional shifts in the soil microbiota (bacteria, fungi and viruses) across burn severity gradients (low, moderate and high severity) 1 yr post fire in coniferous forests in Colorado and Wyoming, USA. We found severity-dependent increases of Actinobacteria encoding genes for heat resistance, fast growth, and pyrogenic carbon utilization that might enhance post-fire survival. We report that increased burn severity led to the loss of ectomycorrhizal fungi and less tolerant microbial taxa. Viruses remained active in post-fire soils and probably influenced carbon cycling and biogeochemistry via turnover of biomass and ecosystem-relevant auxiliary metabolic genes. Our genome-resolved analyses link post-fire soil microbial taxonomy to functions and reveal the complexity of post-fire soil microbiome activity.

Challenges in quantifying and characterizing dissolved organic carbon: Sampling, isolation, storage, and analysis

Despite the advancements in analytical techniques, there are still great challenges and difficulties in accurately and effectively quantifying and characterizing dissolved organic carbon (DOC) in environmental samples. The objectives of this review paper are (a) to understand the roles and variability of DOC along the water continuum; (b) to identify the constraints, inconsistences, limitations, and artifacts in DOC characterization; and (c) to provide recommendations and remarks to improve the analytical accuracy. For the first objective, we summarize the four ecological and engineering roles of DOC along the water continuum from source water to municipal utility, including nutrients and energy sources, controlling the fates of micropollutants, buffering capacity, and treatability and precursors of disinfection byproducts. We also discuss three major challenges in DOC analysis, including spatial and temporal variations, degradability and stability, and unknown structures and formulas. For the second objective, we review the procedures and steps in DOC analysis, including sampling in diverse environmental matrices, isolation of DOC fraction, storage and preservation techniques, and analyses on bulk chemical characteristics. We list and discuss the available options and evaluate the advantages and disadvantages of each choice. Last, we provide recommendations and remarks for each stage: sampling, isolation, storage, and analysis.

Lake reclamation alters molecular-level characteristics of lacustrine dissolved organic matter - A study of nine lakes in the Yangtze Plain, China

In recent decades, the reclamation of lakes has captured 42% of the total lake area of the Yangtze Plain in China and introduced additional pressure on lacustrine water quality. While lacustrine dissolved organic matter (DOM) is critical in regulating biogeochemical processing and aquatic biodiversity, the impact of reclamation on the molecular-level characteristics of lacustrine DOM remains unexplored. Here, the DOM characteristics altered by reclamation practices in the Yangtze Plain lakes were investigated using fluorescence spectroscopy, nuclear magnetic resonance spectroscopy, and Fourier transform ion cyclotron resonance mass spectrometry. Results demonstrated that reclamation not only elevated the quantity (on average +32%) but also altered the characteristics and composition of lacustrine DOM. Compared to the natural water sites close by, reclamation sites did not significantly alter the DOM aromaticity but significantly lowered the average molecular weight and increased the biolability of DOM. The chromophoric DOM and humic-like fluorescent components were remarkably elevated, but not the protein-like fluorescent components. More lipid-like and condensed aromatic-like components were detected in the lacustrine DOM as compared to the lignin-like, carbohydrate-like, and protein-like components, which may be driven by the increased microbial processing. Overall, the significant alteration in characteristics and composition of lacustrine DOM highlights the potential impact of reclamation on the DOM biogeochemical cycle and the environmental quality in aquatic ecosystems.

Improved Dynamic Range, Resolving Power, and Sensitivity Achievable with FT-ICR Mass Spectrometry at 21 T Reveals the Hidden Complexity of Natural Organic Matter

Fourier transform ion-cyclotron resonance mass spectrometry (FT-ICR MS) is the only mass analyzer that can resolve the molecular complexity of natural organic matter at the level of elemental composition assignment. Here, we leverage the high dynamic range, resolving power, resistance to peak coalescence, and maximum ion number and ion trapping duration in a custom built, 21 tesla hybrid linear ion trap /FT-ICR mass spectrometer for a dissolved organic matter standard (Suwanne River Fulvic Acid). We compare the effect of peak-picking threshold (3σ, 4σ, 5σ, and 6σ) on number of elemental composition assignments, mass measurement accuracy, and dynamic range for a 6.3 s transient across the mass range of m/z 200-1200 that comprises the highest achieved resolving power broadband FT-ICR mass spectrum collected to date. More than 36 000 species are assigned with signal magnitude greater than 3σ at root-mean-square mass error of 36 ppb, the most species identified reported to date for dissolved organic matter. We identify ¹⁸O and ¹⁷O isotopologues and resolve isobaric overlaps on the order of a few electrons across a wide mass range (up to m/z 1000) leveraging mass resolving powers (3 000 000 at m/z 200) only achievable by 21 T FT-ICR MS and increased by ∼30% through absorption mode data processing. Elemental compositions unique to the 3σ span a wide compositional range of aromaticity not detected at higher peak-picking thresholds. Furthermore, we leverage the high dynamic range at 21 T FT-ICR MS to provide a molecular catalogue of a widely utilized reference standard (SRFA) to the analytical community collected on the highest performing mass analyzer for complex mixture analysis to date. This instrument is available free of charge to scientists worldwide.

Development of an Internal Calibration Algorithm for Ultrahigh-Resolution Mass Spectra of Dissolved Organic Matter

In order to obtain a spectrum with high mass accuracy, an internal calibration of Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) is inevitable. This in turn is critical for subsequent data processing and is generally performed using the commercial instrument software DataAnalysis in the benchmark calibration mode. However, no methodological study has systemically addressed the automated internal calibration of FTICR-MS spectra for dissolved organic matter (DOM) from different sources such as terrestrial and aquatic environments. In this study, a new piecewise algorithm, FTMSCalibrate, was developed to automatically calibrate FTICR-MS spectra in both positive and negative ion modes. FTMSCalibrate was found to reproduce 91.7% ± 4.4% (referred to as the true positive ratio) of the chemical formulas obtained by calibration using manual DataAnalysis. In addition to significantly reducing the mass error, FTMSCalibrate is more accurate in terms of the molecular formula assignment for low m/z peaks than Formularity and MFAssignR. FTMSCalibrate was compatible with deprotonated ions for FTICR-MS spectra in the negative ion mode as well as protonated and adduct ions, including Na- and K-adducts, for FTICR-MS spectra in the positive ion mode. These results suggest that FTMSCalibrate publicly available herein is a robust alternative for the internal calibration of FTICR-MS spectra during postdata processing and will facilitate DOM analysis by FTICR-MS.

Microbial and Environmental Processes Shape the Link between Organic Matter Functional Traits and Composition

Dissolved organic matter (DOM) is a large and complex mixture of molecules that fuels microbial metabolism and regulates biogeochemical cycles. Individual DOM molecules have unique functional traits, but how their assemblages vary deterministically under global change remains poorly understood. Here, we examine DOM and associated bacteria in 300 aquatic microcosms deployed on mountainsides that span contrasting temperatures and nutrient gradients. Based on molecular trait dimensions of reactivity and activity, we partition the DOM composition into labile-active, recalcitrant-active, recalcitrant-inactive, and labile-inactive fractions and quantify the relative influences of deterministic and stochastic processes governing the assembly of each. At both subtropical and subarctic study sites, the assembly of labile or recalcitrant molecules in active fractions is primarily governed by deterministic processes, while stochastic processes are more important for the assembly of molecules within inactive fractions. Surprisingly, the importance of deterministic selection increases with global change gradients for recalcitrant molecules in both active and inactive fractions, and this trend is paralleled by changes in the deterministic assembly of microbial communities and environmental filtering, respectively. Together, our results highlight the shift in focus from potential reactivity to realized activity and indicate that active and inactive fractions of DOM assemblages are structured by contrasting processes, and their recalcitrant components are consistently sensitive to global change. Our study partitions the DOM molecular composition across functional traits and links DOM with microbes via a shared ecological framework of assembly processes. This integrated approach opens new avenues to understand the assembly and turnover of organic carbon in a changing world.

Molecular characterization of dissolved organic nitrogen and phosphorus in agricultural runoff and surface waters

Agricultural runoff is a significant contributor to nitrogen (N) and phosphorus (P) pollution in water bodies. Limited information is available about the molecular characteristics of the dissolved organic N (DON) and P (DOP) species in the agricultural runoff and surface waters. We employed Fourier Transform-Ion Cyclotron Resonance-Mass Spectrometry (FT-ICR-MS) to investigate the changes in the molecular characteristics of DON and DOP at three watershed positions (upstream water, runoff from agricultural fields, and downstream waters). Across three watershed locations, more-bioavailable compounds (such as amino sugars, carbohydrates, lipids, and proteins) accounted for <5% of DON and 4-31% of DOP molecules, whereas less-bioavailable compounds (such as lignin, tannins, condensed hydrocarbons, and unsaturated hydrocarbons) were >95% of DON and 69-96% of DOP. Of the dissolved organic matter, runoff waters from agricultural fields contained the greatest proportion of DON formulas (20-25%) than upstream (18%) and downstream (13-14%) waters, indicating the presence of a greater diversity of DON species in the runoff. Various nutrient sources present in agricultural fields such as crop residues, soil organic matter, and transformed fertilizers likely contributed to the diverse composition of DON and DOP in the runoff, which were likely altered as the surface water traversed along the flow pathways in the watershed. The presence of more-bioavailable molecules detected in upstream compared to agricultural runoff and downstream waters suggests that photochemical and/or microbial processes likely altered the characteristics of DON and DOP compounds. The findings of this study increase our understanding of DON and DOP compounds lability and transformations in runoff and surface waters , which may be useful in quantifying the contribution of organic N and P sources to water quality impairment in aquatic ecosystems.

Ecological networks of dissolved organic matter and microorganisms under global change

Microbes regulate the composition and turnover of organic matter. Here we developed a framework called Energy-Diversity-Trait integrative Analysis to quantify how dissolved organic matter and microbes interact along global change drivers of temperature and nutrient enrichment. Negative and positive interactions suggest decomposition and production processes of organic matter, respectively. We applied this framework to manipulative field experiments on mountainsides in subarctic and subtropical climates. In both climates, negative interactions of bipartite networks were more specialized than positive interactions, showing fewer interactions between chemical molecules and bacterial taxa. Nutrient enrichment promoted specialization of positive interactions, but decreased specialization of negative interactions, indicating that organic matter was more vulnerable to decomposition by a greater range of bacteria, particularly at warmer temperatures in the subtropical climate. These two global change drivers influenced specialization of negative interactions most strongly via molecular traits, while molecular traits and bacterial diversity similarly affected specialization of positive interactions.

Microbes are intimately linked with the fate of organic matter. Here the authors develop an ecological network framework and show how microbes and dissolved organic matter interact along global change drivers of temperature and nutrient enrichment via manipulative field experiments on mountains.

Implications of sample treatment on characterization of riverine dissolved organic matter

High-resolution mass spectrometry techniques are widely used in the environmental sciences to characterize natural organic matter and, when utilizing these instruments, researchers must make multiple decisions regarding sample pre-treatment and the instrument ionization mode. To identify how these choices alter organic matter characterization and resulting conclusions, we analyzed a collection of 17 riverine samples from East River, CO (USA) under four PPL-based Solid Phase Extraction (SPE) treatment and electrospray ionization polarity (e.g., positive and negative) combinations: SPE (+), SPE (-), non-SPE (-), and non-SPE (+). The greatest number of formula assignments were achieved with SPE-treated samples due to the removal of compounds that could interfere with ionization. Furthermore, the SPE (-) treatment captured the most formulas across the widest chemical compound diversity. In addition to a reduced number of assigned formulas, the non-SPE datasets resulted in altered thermodynamic interpretations that could cascade into incomplete assumptions about the availability of organic matter pools for heterotrophic microbial respiration. Thus, we infer that the SPE (-) treatment is the best single method for characterizing environmental organic matter pools unless the focus is on lipid-like compounds, in which case we recommend a combination of SPE (-) and SPE (+) to adequately characterize these molecules.

Disinfection byproducts formed during drinking water treatment reveal an export control point for dissolved organic matter in a subalpine headwater stream

Changes in climate, season, and vegetation can alter organic export from watersheds. While an accepted tradeoff to protect public health, disinfection processes during drinking water treatment can adversely react with organic compounds to form disinfection byproducts (DBPs). By extension, DBP monitoring can yield insights into hydrobiogeochemical dynamics within watersheds and their implications for water resource management. In this study, we analyzed temporal trends from a water treatment facility that sources water from Coal Creek in Crested Butte, Colorado. These trends revealed a long-term increase in haloacetic acid and trihalomethane formation over the period of 2005-2020. Disproportionate export of dissolved organic carbon and formation of DBPs that exceeded maximum contaminant levels were consistently recorded in association with late spring freshet. Synoptic sampling of the creek in 2020 and 2021 identified a biogeochemical hotspot for organic carbon export in the upper domain of the watershed that contained a prominent fulvic acid-like fluorescent signature. DBP formation potential analyses from this domain yielded similar ratios of regulated DBP classes to those formed at the drinking water facility. Spectrometric qualitative analyses of pre and post-reacted waters with hypochlorite indicated lignin-like and condensed hydrocarbon-like molecules were the major reactive chemical classes during chlorine-based disinfection. This study demonstrates how drinking water quality archives combined with synoptic sampling and targeted analyses can be used to identify and understand export control points for dissolved organic matter. This approach could be applied to identify and characterize analogous watersheds where seasonal or climate-associated organic matter export challenge water treatment disinfection and by extension inform watershed management and drinking water treatment.

Inferring the Contribution of Microbial Taxa and Organic Matter Molecular Formulas to Ecological Assembly

Understanding the mechanisms underlying the assembly of communities has long been the goal of many ecological studies. While several studies have evaluated community wide ecological assembly, fewer have focused on investigating the impacts of individual members within a community or assemblage on ecological assembly. Here, we adapted a previous null model β-nearest taxon index (βNTI) to measure the contribution of individual features within an ecological community to overall assembly. This new metric, called feature-level βNTI (βNTI_feat), enables researchers to determine whether ecological features (e.g., individual microbial taxa) contribute to divergence, convergence, or have insignificant impacts across spatiotemporally resolved metacommunities or meta-assemblages. Using βNTI_feat, we revealed that unclassified microbial lineages often contributed to community divergence while diverse groups (e.g., Crenarchaeota, Alphaproteobacteria, and Gammaproteobacteria) contributed to convergence. We also demonstrate that βNTI_feat can be extended to other ecological assemblages such as organic molecules comprising organic matter (OM) pools. OM had more inconsistent trends compared to the microbial community though CHO-containing molecular formulas often contributed to convergence, while nitrogen and phosphorus-containing formulas contributed to both convergence and divergence. A network analysis was used to relate βNTI_feat values from the putatively active microbial community and the OM assemblage and examine potentially common contributions to ecological assembly across different communities/assemblages. This analysis revealed that P-containing formulas often contributed to convergence/divergence separately from other ecological features and N-containing formulas often contributed to assembly in coordination with microorganisms. Additionally, members of Family Geobacteraceae were often observed to contribute to convergence/divergence in conjunction with both N- and P-containing formulas, suggesting a coordinated ecological role for family members and the nitrogen/phosphorus cycle. Overall, we show that βNTI_feat offers opportunities to investigate the community or assemblage members, which shape the phylogenetic or functional landscape, and demonstrate the potential to evaluate potential points of coordination across various community types.

Constellation: An Open-Source Web Application for Unsupervised Systematic Trend Detection in High-Resolution Mass Spectrometry Data

The increasing popularity of high-resolution mass spectrometry has led to many custom software solutions to process, interpret, and reveal information from high-resolution mass spectra. Although there are numerous software packages for peak-picking, calibration, and formula-finding, there are additional layers of information available when it comes to detecting repeated motifs from polymers or molecules with repeating structures or products of chemical or biochemical transformations that exhibit systematic, serial chemical changes of mass. Constellation is an open-source, Python-based web application that allows the user first to expand their high-resolution mass data into the mass defect space, after which a trend finding algorithm is used for supervised or unsupervised detection of repeating motifs. Many adjustable parameters allow the user to tailor their trend-search to target particular chemical moieties or repeating units, or search for all potential motifs within certain limits. The algorithm has a built-in optimization routine to provide a good starting point for the main trend finding parameters before user customization. Visualization tools allow interrogation of the data and any trends/patterns to a highly specific degree and save publication-quality images directly from the interface. As Constellation is deployed as a web application, it is easily used by anyone with a web browser; no software download or high-powered computer is required, as computations are performed on a remote high-powered data server run by our group.

Natural organic matter composition and nanomaterial surface coating determine the nature of platinum nanomaterial-natural organic matter corona

Natural organic matter corona (NOM corona) is an interfacial area between nanomaterials (NMs) and the surrounding environment, which gives rise to NMs' unique surface identity. While the importance of the formation of natural organic matter (NOM) corona on engineered nanomaterials (NMs) to NM behavior, fate, and toxicity has been well-established, the understanding of how NOM molecular properties affect NOM corona composition remains elusive due to the complexity and heterogeneity of NOM. This is further complicated by the variation of NOMs from different origins. Here we use eight NOM isolates of different molecular composition and ultrahigh resolution Fourier-transform ion cyclotron resonance-mass spectrometry (ESI-FT-ICR-MS) to determine the molecular composition of platinum NM-NOM corona as a function of NOM composition and NM surface coating. We observed that the composition of PtNM-NOM corona varied with the composition of the original NOM. The percentage of NOM formulas that formed PVP-PtNM-NOM corona was higher than those formed citrate-PtNM-NOM corona, due to increased sorption of NOM formulas, in particular condensed hydrocarbons, to the PVP coating. The relative abundance of heteroatom formulas (CHON, CHOS, and CHOP) was higher in the PVP-PtNM-NOM corona than in citrate-PtNM-corona which was in turn higher than those in the original NOM isolate, indicating preferential partitioning of heteroatom-rich molecules to NM surfaces. The relative abundance of CHO, CHON, CHOS, CHOP and condensed hydrocarbons in PtNM-NOM corona increased with their increase in NOM isolates. Furthermore, PtNM-NOM corona is rich with compounds with high molecular weight. This study demonstrates that the composition and properties of PtNM-NOM corona depend on NOM molecular properties and PtNM surface coating. The results here provide evidence of molecular interactions between NOM and NMs, which are critical to understanding NM colloidal properties (e.g., surface charge and stability), interaction forces (e.g., van der Waals and hydrophobic), environmental behaviors (e.g., aggregation, dissolution, sulfidation, etc.), and biological effects (e.g., uptake, bioaccumulation, and toxicity).

Chemical characterization of dissolved organic matter as disinfection byproduct precursors by UV/fluorescence and ESI FT-ICR MS after smoldering combustion of leaf needles and woody trunks of pine (Pinus jeffreyi)

Forested land plays an essential role in water supply across the United States (US). Smoldering commonly existing in wildfires contributes significantly to biomass consumption and gas emission, but its influence on source water quality has been rarely studied. Here, we investigated the impact of smoldering temperature (i.e., no burn, 250, 400, and 600 °C) on the nutrients, elements, and dissolved organic matter (DOM) of water extracts from the residues of the leaf needles and woody trunks of pine (Pinus jeffreyi) under the lab-simulated smoldering fire. Results showed the increase of pH and the yields of the dominated exchangeable cations of K+ and Mg2+, P, PO43--P, and SO42- with increasing temperature increasing from 250 to 600 °C, whereas significant decreases in the fraction of dissolved organic C in residue C with increasing temperature and the yields of dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) after burnings. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) presented consistent results with UV/fluorescence, suggesting that the unburned materials contained more biodegradable tyrosine/tryptophan/soluble microbial byproduct-like compounds with high molecular weight (MW), whereas the 600 °C-smoldering materials composed of more aromatic, humified, fulvic/humic acid-like, and oxidized compounds with a potentially high density of C=C bonds had less reactivity in forming trihalomethanes (THMs) and haloacetonitriles (HANs). Our study indicates the smoldering-dominated prescribed fire as a potential forest management strategy for reducing biomass fuel and disinfection byproducts (DBPs) precursors in source water from forested lands.

Microbial Communities Influence Soil Dissolved Organic Carbon Concentration by Altering Metabolite Composition

Rapid microbial growth in the early phase of plant litter decomposition is viewed as an important component of soil organic matter (SOM) formation. However, the microbial taxa and chemical substrates that correlate with carbon storage are not well resolved. The complexity of microbial communities and diverse substrate chemistries that occur in natural soils make it difficult to identify links between community membership and decomposition processes in the soil environment. To identify potential relationships between microbes, soil organic matter, and their impact on carbon storage, we used sand microcosms to control for external environmental factors such as changes in temperature and moisture as well as the variability in available carbon that exist in soil cores. Using Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) on microcosm samples from early phase litter decomposition, we found that protein- and tannin-like compounds exhibited the strongest correlation to dissolved organic carbon (DOC) concentration. Proteins correlated positively with DOC concentration, while tannins correlated negatively with DOC. Through random forest, neural network, and indicator species analyses, we identified 42 bacterial and 9 fungal taxa associated with DOC concentration. The majority of bacterial taxa (26 out of 42 taxa) belonged to the phylum Proteobacteria while all fungal taxa belonged to the phylum Ascomycota. Additionally, we identified significant connections between microorganisms and protein-like compounds and found that most taxa (12/14) correlated negatively with proteins indicating that microbial consumption of proteins is likely a significant driver of DOC concentration. This research links DOC concentration with microbial production and/or decomposition of specific metabolites to improve our understanding of microbial metabolism and carbon persistence.

Elevated temperatures drive abiotic and biotic degradation of organic matter in a peat bog under oxic conditions

Understanding the effects of elevated temperatures on soil organic matter (SOM) decomposition pathways in northern peatlands is central to predicting their fate under future warming. Peatlands role as carbon (C) sink is dependent on both anoxic conditions and low temperatures that limit SOM decomposition. Previous studies have shown that elevated temperatures due to climate change can disrupt peatland's C balance by enhancing SOM decomposition and increasing CO₂ emissions. However, little is known about how SOM decomposition pathways change at higher temperatures. Here, we used an integrated research approach to investigate the mechanisms behind enhanced CO₂ emissions and SOM decomposition under elevated temperatures of surface peat soil collected from a raised and Sphagnum dominated mid-continental bog (S1 bog) peatland at the Marcel Experimental Forest in Minnesota, USA, incubated under oxic conditions at three different temperatures (4, 21, and 35 °C). Our results indicated that elevated temperatures could destabilize peatland's C pool via a combination of abiotic and biotic processes. In particular, temperature-driven changes in redox conditions can lead to abiotic destabilization of Fe-organic matter (phenol) complexes, previously an underestimated decomposition pathway in peatlands, leading to increased CO₂ production and accumulation of polyphenol-like compounds that could further inhibit extracellular enzyme activities and/or fuel the microbial communities with labile compounds. Further, increased temperatures can alter strategies of microbial communities for nutrient acquisition via changes in the activities of extracellular enzymes by priming SOM decomposition, leading to enhanced CO₂ emission from peatlands. Therefore, coupled biotic and abiotic processes need to be incorporated into process-based climate models to predict the fate of SOM under elevated temperatures and to project the likely impacts of environmental change on northern peatlands and CO₂ emissions.