Identification of Mercury and Dissolved Organic Matter Complexes Using Ultrahigh Resolution Mass Spectrometry

The chemical speciation and bioavailability of mercury (Hg) is markedly influenced by its complexation with naturally dissolved organic matter (DOM) in aquatic environments. To date, however, analytical methodologies capable of identifying such complexes are scarce. Here, we utilize ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) coupled with electrospray ionization to identify individual Hg–DOM complexes. The measurements were performed by direct infusion of DOM in a 1:1 methanol:water solution at a Hg to dissolved organic carbon (DOC) molar ratio of 3 × 10^–4. Heteroatomic molecules, especially those containing multiple S and N atoms, were found to be among the most important in forming strong complexes with Hg. Major Hg–DOM complexes of C₁₀H₂₁N₂S₄Hg⁺ and C₈H₁₇N₂S₄Hg⁺ were identified based on both the exact molecular mass and patterns of Hg stable isotope distributions detected by FTICR-MS. Density functional theory was used to predict the solution-phase structures of candidate molecules. These findings represent the first step to unambiguously identify specific DOM molecules in Hg binding, although future studies are warranted to further optimize and validate the methodology so as to explore detailed molecular compositions and structures of Hg–DOM complexes that affect biological uptake and transformation of Hg in the environment

Molecular Insights into Arctic Soil Organic Matter Degradation under Warming

Molecular composition of the Arctic soil organic carbon (SOC) and its susceptibility to microbial degradation are uncertain due to heterogeneity and unknown SOC compositions. Using ultrahigh-resolution mass spectrometry, we determined the susceptibility and compositional changes of extractable dissolved organic matter (EDOM) in an anoxic warming incubation experiment (up to 122 days) with a tundra soil from Alaska (United States). EDOM was extracted with 10 mM NH₄HCO₃ from both the organic- and mineral-layer soils during incubation at both −2 and 8 °C. Based on their O:C and H:C ratios, EDOM molecular formulas were qualitatively grouped into nine biochemical classes of compounds, among which lignin-like compounds dominated both the organic and the mineral soils and were the most stable, whereas amino sugars, peptides, and carbohydrate-like compounds were the most biologically labile. These results corresponded with shifts in EDOM elemental composition in which the ratios of O:C and N:C decreased, while the average C content in EDOM, molecular mass, and aromaticity increased after 122 days of incubation. This research demonstrates that certain EDOM components, such as amino sugars, peptides, and carbohydrate-like compounds, are disproportionately more susceptible to microbial degradation than others in the soil, and these results should be considered in SOC degradation models to improve predictions of Arctic climate feedbacks

Indexing Permafrost Soil Organic Matter Degradation Using High-Resolution Mass Spectrometry

<div><p>Microbial degradation of soil organic matter (SOM) is a key process for terrestrial carbon cycling, although the molecular details of these transformations remain unclear. This study reports the application of ultrahigh resolution mass spectrometry to profile the molecular composition of SOM and its degradation during a simulated warming experiment. A soil sample, collected near Barrow, Alaska, USA, was subjected to a 40-day incubation under anoxic conditions and analyzed before and after the incubation to determine changes of SOM composition. A CHO index based on molecular C, H, and O data was utilized to codify SOM components according to their observed degradation potentials. Compounds with a CHO index score between –1 and 0 in a water-soluble fraction (WSF) demonstrated high degradation potential, with a highest shift of CHO index occurred in the N-containing group of compounds, while similar stoichiometries in a base-soluble fraction (BSF) did not. Additionally, compared with the classical H:C vs O:C van Krevelen diagram, CHO index allowed for direct visualization of the distribution of heteroatoms such as N in the identified SOM compounds. We demonstrate that CHO index is useful not only in characterizing arctic SOM at the molecular level but also enabling quantitative description of SOM degradation, thereby facilitating incorporation of the high resolution MS datasets to future mechanistic models of SOM degradation and prediction of greenhouse gas emissions.</p></div

Heatmaps for CHO index as a function of molecular mass of extracted SOM compounds before and after the soil warming experiment.

<p>The color bar represents the relative abundance of compounds identified in each of the SOM extract: <b>(a)</b> WSF0, <b>(b)</b> WSF40, <b>(c)</b> BSF0, and <b>(d)</b> BSF40. A positive correlation between CHO index and mass can be observed for mass > 600 Da.</p

Molecular distribution of extracted SOM compounds from a 40-day soil warming incubation experiment.

<p>(a) Box-and-whisker plots of the mass distribution of SOM compounds, including the base-soluble fraction (BSF) at day 0 (BSF0) and day 40 (BSF40) and the water-soluble fraction (WSF) at day 0 (WSF0) and day 40 (WSF40). <b>(b and c)</b> van Krevelen diagram along with CHO index showing the molecular distribution of WSF SOM compounds before (b) and after (c) incubation. <b>(d)</b> Percentages of molecular formulae identified with CHO index values between -2 and 2 before and after soil incubation and are normalized to the total number of formulae displayed in (b) and (c). Compound classes are labeled above colored bars as follows: (A) lipids, (B) unsaturated hydrocarbons, (C) peptides, (D) aminosugars, (E) carbohydrates, (F) lignin, (G) condensed hydrocarbons, (H) tannins.</p

Heatmaps for CHO index as a function of the number of N atoms in extracted SOM compounds identified in (a) WSF0, (b) WSF40, (c) BSF0, and (d) BSF40.

<p>The color bar represents the relative abundance of compounds identified in each of the extract. Note the “island” formations observed for even numbers of N atoms in the BSF samples.</p

Number weighted (Mean _#) and magnitude-weighted mean (Mean _w) properties for SOM extracts from a simulated soil warming experiment.

<p>The CHO index was calculated as (2×[<i>O</i>]–[<i>H</i>])/[<i>C</i>].</p

Comprehensive Quantitative Analysis of Ovarian and Breast Cancer Tumor Peptidomes

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of tumor peptidomes, the intracellular and intercellular products of protein degradation, has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the information on these smaller protein degradation products from tumors for biomarker discovery and cancer biology studies have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification and the prevalence of confounding factors and biases associated with sample handling and processing. Herein, we have developed an effective and robust analytical platform for comprehensive analyses of tissue peptidomes, which is suitable for high-throughput quantitative studies. The reproducibility and coverage of the platform, as well as the suitability of clinical ovarian tumor and patient-derived breast tumor xenograft samples with postexcision delay of up to 60 min before freezing for peptidomics analysis, have been demonstrated. Moreover, our data also show that the peptidomics profiles can effectively separate breast cancer subtypes, reflecting tumor-associated protease activities. Peptidomics complements results obtainable from conventional bottom-up proteomics and provides insights not readily obtainable from such approaches

Comprehensive Quantitative Analysis of Ovarian and Breast Cancer Tumor Peptidomes

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of tumor peptidomes, the intracellular and intercellular products of protein degradation, has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the information on these smaller protein degradation products from tumors for biomarker discovery and cancer biology studies have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification and the prevalence of confounding factors and biases associated with sample handling and processing. Herein, we have developed an effective and robust analytical platform for comprehensive analyses of tissue peptidomes, which is suitable for high-throughput quantitative studies. The reproducibility and coverage of the platform, as well as the suitability of clinical ovarian tumor and patient-derived breast tumor xenograft samples with postexcision delay of up to 60 min before freezing for peptidomics analysis, have been demonstrated. Moreover, our data also show that the peptidomics profiles can effectively separate breast cancer subtypes, reflecting tumor-associated protease activities. Peptidomics complements results obtainable from conventional bottom-up proteomics and provides insights not readily obtainable from such approaches

Comprehensive Quantitative Analysis of Ovarian and Breast Cancer Tumor Peptidomes

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of tumor peptidomes, the intracellular and intercellular products of protein degradation, has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the information on these smaller protein degradation products from tumors for biomarker discovery and cancer biology studies have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification and the prevalence of confounding factors and biases associated with sample handling and processing. Herein, we have developed an effective and robust analytical platform for comprehensive analyses of tissue peptidomes, which is suitable for high-throughput quantitative studies. The reproducibility and coverage of the platform, as well as the suitability of clinical ovarian tumor and patient-derived breast tumor xenograft samples with postexcision delay of up to 60 min before freezing for peptidomics analysis, have been demonstrated. Moreover, our data also show that the peptidomics profiles can effectively separate breast cancer subtypes, reflecting tumor-associated protease activities. Peptidomics complements results obtainable from conventional bottom-up proteomics and provides insights not readily obtainable from such approaches