1H-13C Heteronuclear Single Quantum Coherence Research Articles

Improving quality of analysis by suppression of unwanted signals through band-selective excitation in NMR spectroscopy for metabolomics studies.

Nuclear Magnetic Resonance (NMR) spectroscopy stands as a preeminent analytical tool in the field of metabolomics. Nevertheless, when it comes to identifying metabolites present in scant amounts within various types of complex mixtures such as plants, honey, milk, and biological fluids and tissues, NMR-based metabolomics presents a formidable challenge. This predicament arises primarily from the fact that the signals emanating from metabolites existing in low concentrations tend to be overshadowed by the signals of highly concentrated metabolites within NMR spectra. The aim of this study is to tackle the issue of intense sugar signals overshadowing the desired metabolite signals, an optimal pulse sequence with band-selective excitation has been proposed for the suppression of sugar's moiety signals (SSMS). This sequence serves the crucial purpose of suppressing unwanted signals, with a particular emphasis on mitigating the interference caused by sugar moieties' signals. We have implemented this comprehensive approach to various NMR techniques, including 1D 1H presaturation (presat), 2D J-resolved (RES), 2D 1H-1H Total Correlation Spectroscopy (TOCSY), and 2D 1H-13C Heteronuclear Single Quantum Coherence (HSQC) for the samples of dates-flesh, honey, a standard stock solution of glucose, and nine amino acids, and commercial fetal bovine serum (FBS). The outcomes of this approach were significant. The suppression of the high-intensity sugar signals has considerably enhanced the visibility and sensitivity of the signals emanating from the desired metabolites. This, in turn, enables the identification of a greater number of metabolites. Additionally, it streamlines the experimental process, reducing the time required for the comparative quantification of metabolites in statistical studies in the field of metabolomics.

Cutting without a Knife: A Slice-Selective 2D 1H-13C HSQC NMR Sequence for the Analysis of Inhomogeneous Samples.

Nuclear magnetic resonance (NMR) is a powerful technique with applications ranging from small molecule structure elucidation to metabolomics studies of living organisms. Typically, solution-state NMR requires a homogeneous liquid, and the whole sample is analyzed as a single entity. While adequate for homogeneous samples, such an approach is limited if the composition varies as would be the case in samples that are naturally heterogeneous or layered. In complex samples such as living organisms, magnetic susceptibility distortions lead to broad 1H line shapes, and thus, the additional spectral dispersion afforded by 2D heteronuclear experiments is often required for metabolite discrimination. Here, a novel, slice-selective 2D, 1H-13C heteronuclear single quantum coherence (HSQC) sequence was developed that exclusively employs shaped pulses such that only spins in the desired volume are perturbed. In turn, this permits multiple volumes in the tube to be studied during a single relaxation delay, increasing sensitivity and throughput. The approach is first demonstrated on standards and then used to isolate specific sample/sensor elements from a microcoil array and finally study slices within a living earthworm, allowing metabolite changes to be discerned with feeding. Overall, slice-selective NMR is demonstrated to have significant potential for the study of layered and other inhomogeneous samples of varying complexity. In particular, its ability to select subelements is an important step toward developing microcoil receive-only arrays to study environmental toxicity in tiny eggs, cells, and neonates, whereas localization in larger living species could help better correlate toxin-induced biochemical responses to the physical localities or organs involved.

Application of 15N-Edited 1H-13C Correlation NMR Spectroscopy─Toward Fragment-Based Metabolite Identification and Screening via HCN Constructs.

Many key building blocks of life contain nitrogen moieties. Despite the prevalence of nitrogen-containing metabolites in nature, 15N nuclei are seldom used in NMR-based metabolite assignment due to their low natural abundance and lack of comprehensive chemical shift databases. However, with advancements in isotope labeling strategies, 13C and 15N enriched metabolites are becoming more common in metabolomic studies. Simple multidimensional nuclear magnetic resonance (NMR) experiments that correlate 1H and 15N via single bond 1JNH or multiple bond 2-3JNH couplings using heteronuclear single quantum coherence (HSQC) or heteronuclear multiple bond coherence are well established and routinely applied for structure elucidation. However, a 1H-15N correlation spectrum of a metabolite mixture can be difficult to deconvolute, due to the lack of a 15N specific database. In order to bridge this gap, we present here a broadband 15N-edited 1H-13C HSQC NMR experiment that targets metabolites containing 15N moieties. Through this approach, nitrogen-containing metabolites, such as amino acids, nucleotide bases, and nucleosides, are identified based on their 13C, 1H, and 15N chemical shift information. This approach was tested and validated using a [15N, 13C] enriched Daphnia magna (water flea) metabolite extract, where the number of clearly resolved 15N-containing peaks increased from only 11 in a standard HSQC to 51 in the 15N-edited HSQC, and the number of obscured peaks decreased from 59 to just 7. The approach complements the current repertoire of NMR techniques for mixture deconvolution and holds considerable potential for targeted metabolite NMR in 15N, 13C enriched systems.

1H and 13C NMR spectral assignments for low-concentration bile acids in biological samples

Bile acids are the main body of enterohepatic circulation in vivo. They have essential functions such as emulsifying fat, bacteriostasis and regulating multiple metabolic pathways as signal molecules. However, the assignments of NMR signals for some low-concentration bile acids are still needed. This study combined 1D nuclear magnetic resonance (NMR) and 2D NMR techniques including 1H–1H correlation spectroscopy (COSY), 1H–1H total correlation spectroscopy (TOCSY), 1H J-resolved spectroscopy (J-Res), 1H–13C heteronuclear single quantum coherence spectroscopy (HSQC), and 1H–13C heteronuclear multiple bond correlation spectroscopy (HMBC) to assign the 1H and 13C signals of six bile acids in aqueous solution at physiological pH (∼7.4) and nine bile acids in methanol. These data are of importance to the NMR-based studies on lipid digestion, absorption, and metabolism.

Deciphering the contribution of microbial biomass to the properties of dissolved and particulate organic matter in anaerobic digestates

The recalcitrant structures either from substrate or microbial biomass contained in digestates after anaerobic digestion (AD) highly influence digestate valorization. To properly assess the microbial biomass contribution to the digested organic matter (OM), a combination of characterization methods and the use of various substrate types in anaerobic continuous reactors was required. The use of totally biodegradable substrates allowed detecting soluble microbial products via fluorescence spectroscopy at emission wavelengths of 420 and 460 nm while the protein-like signature was enhanced by the whey protein. During reactors' operation, a transfer of complex compounds to the dissolved OM from the particulate OM was observed through fluorescence applied on biochemical fractionation. Consequently, the fluorescence complexity index of the dissolved OM increased from 0.59–0.60 to 1.06–1.07, whereas it decreased inversely for the extractable soluble from the particulate OM from 1.16–1.19 to 0.42–0.54. Accordingly, fluorescence regional integration showed differences among reactors based on visual inspection and orthogonal partial latent structures (OPLS) analysis. Similarly, the impact of the substrate type and operation time on the particulate OM was revealed by 13C nuclear magnetic resonance using OPLS, providing a good model (R2X = 0.93 and Q2 = 0.8) with a clear time-trend. A high signal resonated at ∼30 ppm attributed to CH2-groups in the aliphatic chain of lipid-like structure besides carbohydrates intensities at 60–110 ppm distinguished the reactor fed with whey protein from the other, which was mostly biomass related. Indeed, this latter displayed a higher presence of peptidoglycan (δH/C: 1.6–2.0/20–25 ppm) derived from microbial biomass by 1H-13C heteronuclear single-quantum coherence (HSQC) nuclear magnetic resonance. Interestingly, the sample distribution obtained by non-metric multidimensional scaling of bacterial communities resembled the attained using 13C NMR properties, opening new research perspectives. Overall, this study discloses the microbial biomass contribution to digestates composition to improve the OM transformation mechanism knowledge.

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H-13C HSQC-Based Metabolomics.

Two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) 1H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D 1H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D 1H-13C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδo) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδo for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D 1H-13C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.

Correlation between Lignin-Carbohydrate Complex Content in Grass Lignins and Phenolic Aldehyde Production by Rapid Spray Ozonolysis.

We provide strong evidence that the amounts of phenolic aldehydes (vanillin and p-hydroxybenzaldehyde, pHB) selectively released during rapid ozonolysis of grass lignins are correlated with the unsubstituted aryl carbons of lignin-carbohydrate complexes present in these lignins. In the case of acetosolv lignin from corn stover, we observed a steady yield of vanillin and pHB (cumulatively ∼5 wt % of the initial lignin). We demonstrate the continuous ozonolysis of the lignin in a spray reactor at ambient temperature and pressure. In sharp contrast, similar ozonolysis of acetosolv lignin from corn cobs resulted in a twofold increase in the combined yield (∼10 wt %) of vanillin and pHB. Structural analysis with 1H-13C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance revealed that signals assigned to unsubstituted aryl carbons of lignin-carbohydrate complexes are quantitatively correlated to phenolic aldehyde production from spray ozonolysis. The ratios of the integrated peak volumes corresponding to coumarates and ferulates in the HSQC spectra of cob and corn stover lignins (SLs) are 2.4 and 2.0, respectively. These ratios are nearly identical to the observed 2.3-fold increase in pHB and 1.8-fold increase in vanillin production rates from corn cob lignin compared to corn SL. Considering that the annual U.S. lignin capacity from these grass lignin sources is ∼60 million MT, the value creation potential from these flavoring agents is conservatively ∼$50 million annually from just 10% of the lignin. These new insights into structure/product correlation and spray reactor characteristics provide rational guidance for developing viable technologies to valorize grass lignins.

Leveraging the HMBC to Facilitate Metabolite Identification.

The accuracy and ease of metabolite assignments from a complex mixture are expected to be facilitated by employing a multispectral approach. The two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) and 2D 1H-1H-total correlation spectroscopy (TOCSY) are the experiments commonly used for metabolite assignments. The 2D 1H-13C HSQC-TOCSY and 2D 1H-13C heteronuclear multiple-bond correlation (HMBC) are routinely used by natural products chemists but have seen minimal usage in metabolomics despite the unique information, the nearly complete 1H-1H and 1H-13C and spin systems provided by these experiments that may improve the accuracy and reliability of metabolite assignments. The use of a 13C-labeled feedstock such as glucose is a routine practice in metabolomics to improve sensitivity and to emphasize the detection of specific metabolites but causes severe artifacts and an increase in spectral complexity in the HMBC experiment. To address this issue, the standard HMBC pulse sequence was modified to include carbon decoupling. Nonuniform sampling was also employed for rapid data collection. A dataset of reference 2D 1H-13C HMBC spectra was collected for 94 common metabolites. 13C-13C spin connectivity was then obtained by generating a covariance pseudo-spectrum from the carbon-decoupled HMBC and the 1H-13C HSQC-TOCSY spectra. The resulting 13C-13C pseudo-spectrum provides a connectivity map of the entire carbon backbone that uniquely describes each metabolite and would enable automated metabolite identification.

Efficient Lignin Fractionation from Scots Pine (Pinus sylvestris) Using Ammonium-Based Protic Ionic Liquid: Process Optimization and Characterization of Recovered Lignin.

Lignin-based chemicals and biomaterials will be feasible alternatives to their fossil-fuel-based counterparts once their breakdown into constituents is economically viable. The existing commercial market for lignin remains limited due to its complex heterogenous structure and lack of extraction/depolymerization techniques. Hence, in the present study, a novel low-cost ammonium-based protic ionic liquid (PIL), 2-hydroxyethyl ammonium lactate [N11H(2OH)][LAC], is used for the selective fractionation and improved extraction of lignin from Scots pine (Pinus sylvestris) softwood biomass (PWB). The optimization of three process parameters, viz., the incubation time, temperature, and biomass:PIL (BM:PIL) ratio, was performed to determine the best pretreatment conditions for lignin extraction. Under the optimal pretreatment conditions (180 °C, 3 h, and 1:3 BM:PIL ratio), [N11H(2OH)][LAC] yielded 61% delignification with a lignin recovery of 56%; the cellulose content of the recovered pulp was approximately 45%. Further, the biochemical composition of the recovered lignin and pulp was determined and the recovered lignin was characterized using 1H-13C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectroscopy, quantitative 31P NMR, gel permeation chromatography (GPC), attenuated total reflectance (ATF)-Fourier transform infrared spectroscopy (ATR-FTIR), and thermal gravimetric analysis (TGA) analysis. Our results reveal that [N11H(2OH)][LAC] is significantly involved in the cleavage of predominant β-O-4' linkages for the generation of aromatic monomers followed by the in situ depolymerization of PWB lignin. The simultaneous extraction and depolymerization of PWB lignin favors the utilization of recalcitrant pine biomass as feedstock for biorefinery schemes.

Metabolomic approaches for the detection of Listeria monocytogenes and Staphylococcus aureus in culture media

This study aimed to determine the changes in metabolites in the culture media of foodborne pathogens using nuclear magnetic resonance (NMR) spectroscopy. Extracellular metabolites in tryptic soy broth inoculated with none, Listeria monocytogenes ATCC 19111, and Staphylococcus aureus ATCC 29213 and 27213 at 37 °C were collected and analyzed by 1D 1H NMR and 2D 1H–13C heteronuclear single quantum coherence (HSQC) NMR. Partial least squares discriminant analysis revealed an excellent variable influence on projection (VIP) score and predictive ability among the different culture media metabolites. Based on presence/absence, five extracellular metabolites (cadaverine, histidine, nicotinate, putrescine, and succinate) were only detected in culture media containing pathogens. According to the pathogen species, L. monocytogenes and S. aureus ATCC 29213 could be distinguished based on changes in 11 metabolites (tyrosine, betaine, glycine, pyroglutamate, fumarate, alanine, putrescine, arginine, inosine, threonine, and sucrose). Moreover, different strains of S. aureus (ATCC 29213 and 27213) were distinguished using hypoxanthine and putrescine. Overall, extracellular metabolites in culture media could be used to detect the presence/absence, species, and even different strains of foodborne pathogens.

Microstructural Analysis of Poly(ethylene-co-propylene-co-1-butene) terpolymers by Multidimensional NMR Spectroscopy

Physical, rheological and optical properties of polymer depend on its molecular microstructure. Specifically, the monomer content and sequence distribution of polymer significantly affect the mechanical performance of the final product. To this base, Nuclear Magnetic Resonance Spectroscopy (NMR) has been introduced as a very powerful technique to study the microstructure of polymer. The purpose of this study was to develop a methodology to investigate the microstructure of poly(ethylene-co-propylene-co-butene) terpolymer by using multidimensional NMR spectroscopy, including pulsed-field-gradient (PFG) 1H-13C heteronuclear single quantum coherence (GHSQC), heteronuclear multiple bond coherence (gHMBC), and heteronuclear single quantum coherence-total correlation spectroscopy (GHSQC-TOCSY). The combination of 1D and 2D NMR experiments, permit the characterization of the complex structure of poly(ethylene-co-propylene-co-butene) terpolymer. The completed 13C NMR assignments provided in this work allow more accurate quantitative determination of the microstructure in these polymers.

Molecular Mechanism of Antimicrobial Excipient-Induced Aggregation in Parenteral Formulations of Peptide Therapeutics.

Antimicrobial preservatives are used as functional excipients in multidose formulations of biological therapeutics to destroy or inhibit the growth of microbial contaminants, which may be introduced by repeatedly administering doses. Antimicrobial agents can also induce the biophysical instability of proteins and peptides, which presents a challenge in optimizing the drug product formulation. Elucidating the structural basis for aggregation aids in understanding the underlying mechanism and can offer valuable knowledge and rationale for designing drug substances and drug products; however, this remains largely unexplored due to the lack of high-resolution characterization. In this work, we utilize solution nuclear magnetic resonance (NMR) as an advanced biophysical tool to study an acylated 31-residue peptide, acyl-peptide A, and its interaction with commonly used antimicrobial agents, benzyl alcohol and m-cresol. Our results suggest that acyl-peptide A forms soluble octamers in the aqueous solution, which tumble slowly due to an increased molecular weight as measured by diffusion ordered spectroscopy and 1H relaxation measurement. The addition of benzyl alcohol does not induce aggregation of acyl-peptide A and has no chemical shift perturbation in 1H-1H NOESY spectra, suggesting no detectable interaction with the peptide. In contrast, the addition of 1% (w/v) m-cresol results in insoluble aggregates composed of 25% (w/w) peptides after a 24-hour incubation at room temperature as quantified by 1H NMR. Interestingly, 1H-13C heteronuclear single-quantum coherence and 1H-1H total correlation experiment spectroscopy have identified m-cresol and peptide interactions at specific residues, including Met, Lys, Glu, and Gln, suggesting that there may be a combination of hydrophobic, hydrogen bonding, and electrostatic interactions with m-cresol driving this phenomenon. These site-specific interactions have promoted the formation of higher-order oligomerization such as dimers and trimers of octamers, eventually resulting in insoluble aggregates. Our study has elucidated a structural basis of m-cresol-induced self-association that can inform the optimized design of drug substances and products. Moreover, it has demonstrated solution NMR as a high-resolution tool to investigate the structure and dynamics of biological drug products and provide an understanding of excipient-induced peptide and protein aggregation.

Upcycling Polytetrahydrofuran to Polyester

Upcycling Polytetrahydrofuran to Polyester

Coacervation between Two Positively Charged Poly(ionic liquid)s.

Complex coacervates are usually formed through electrostatic attraction between oppositely charged polyelectrolytes, with a few exceptions such as coacervates of like-charge proteins and polyelectrolytes, both in vivo and in vitro. Understanding of the preparation and mechanisms of these coacervates is limited. Here, a positively charged poly(ionic liquid), poly(1-vinyl-3-benzylimidazolium chloride) (PILben), is designed that bears benzene rings in repeating units. Fluidic coacervates are prepared by mixing the PILben aqueous solution with a like-charge poly(ionic liquid) named poly(dimethyl diallyl ammonium chloride) (PDDA). The effects of polymer concentration, temperature, and ionic strength in the PILben-PDDA coacervate are studied. Raman spectroscopy and 2D 1 H-13 C heteronuclear single quantum coherence (1 H-13 C HSQC) characterizations verify that the coacervate formation benefits from the cation-π interaction between PILben and PDDA. This work provides principles and understandings of designing coacervates derived from like-charge poly(ionic liquids) with high charge density.

Isolation, Characterization, and Metabolic Profiling of Ceratorhiza hydrophila from the Aquatic Plant Myriophyllum spicatum

The goal of the present study was to investigate the antibacterial properties, enzyme production, and metabolic profiling of a new Ceratorhiza hydrophila strain isolated from the submerged aquatic plant Myriophyllum spicatum. Furthermore, the fungus' morphological characterization and DNA sequencing have been described. The fungus has been identified and submitted to the GenBank as Ceratorhiza hydrophila isolate EG19 and the fungus ID is MK387081. The enzyme analyses showed its ability to produce protease and cellulase enzymes. According to the CSLI standard, the ethyl acetate extract of C. hydrophila showed intermediate antibacterial activity against Streptococcus pneumonia, Micrococcus luteus, and Staphylococcus aureus. Metabolic profiling has been carried out using 700 MHz NMR spectroscopy. Based on the 1H and 1H-13C heteronuclear single quantum coherence (HSQC) NMR data and NMR databases, 23 compounds have been identified. The identified metabolites include 31% amino acids, 9% sugars, 9% amines, 4% sugar alcohols, and 4% alkaloids. This is the first report for the metabolic characterization of C. hydrophila, which gave preliminary information about the fungus. It is expected that our findings not only will pave the way to other perspectives in enormous applications using C. hydrophila as a new promising source of antimicrobial agents and essential metabolites, but also it will be valuable in the classification and chemotaxonomy of the species.

Plasma Metabolite Signature Classifies Male LRRK2 Parkinson's Disease Patients.

Parkinson’s disease (PD) is a progressive neurodegenerative disease, causing loss of motor and nonmotor function. Diagnosis is based on clinical symptoms that do not develop until late in the disease progression, at which point the majority of the patients’ dopaminergic neurons are already destroyed. While many PD cases are idiopathic, hereditable genetic risks have been identified, including mutations in the gene for LRRK2, a multidomain kinase with roles in autophagy, mitochondrial function, transcription, molecular structural integrity, the endo-lysosomal system, and the immune response. A definitive PD diagnosis can only be made post-mortem, and no noninvasive or blood-based disease biomarkers are currently available. Alterations in metabolites have been identified in PD patients, suggesting that metabolomics may hold promise for PD diagnostic tools. In this study, we sought to identify metabolic markers of PD in plasma. Using a 1H-13C heteronuclear single quantum coherence spectroscopy (HSQC) NMR spectroscopy metabolomics platform coupled with machine learning (ML), we measured plasma metabolites from approximately age/sex-matched PD patients with G2019S LRRK2 mutations and non-PD controls. Based on the differential level of known and unknown metabolites, we were able to build a ML model and develop a Biomarker of Response (BoR) score, which classified male LRRK2 PD patients with 79.7% accuracy, 81.3% sensitivity, and 78.6% specificity. The high accuracy of the BoR score suggests that the metabolomics/ML workflow described here could be further utilized in the development of a confirmatory diagnostic for PD in larger patient cohorts. A diagnostic assay for PD will aid clinicians and their patients to quickly move toward a definitive diagnosis, and ultimately empower future clinical trials and treatment options.

1H-13C heteronuclear single quantum coherence NMR evidence for iodination of natural organic matter influencing organo-iodine mobility in the environment

The complex biogeochemical behavior of iodine (I) isotopes and their interaction with natural organic matter (NOM) pose a challenge for transport models. Here, we present results from iodination experiments with humic acid (HA) and fulvic acid (FA) using 1H-13C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectroscopy. Even though not a quantitative approach, 1H-13C HSQC NMR corroborated that iodination of NOM occurs primarily through aromatic electrophilic substitution of proton by I, and also revealed how iodination chemically alters HA and FA in a manner that potentially affects the mobility of iodinated NOM in the environment. Three types of iodination experiments were conducted with HA and FA: a) non-enzymatic iodination by IO3− (pH 3) and I− (pH 4 and 7), b) addition of lactoperoxidase to promote I−-iodination in the presence of the co-substrate, H2O2 (pH 7), and c) addition of laccase for facilitating I−-iodination in the presence of O2, with or without a mediator (pH 4). When mediators or H2O2 were present, extracellular oxidases and peroxidases enhanced I− incorporation into NOM by between 54% and 3400%. Iodination of HA, which was less than that of FA, enhanced HA's stability (inferred from increases in aliphatic compounds, decreases in carbohydrate moieties, and thus increased molecular hydrophobicity) yet reduced HA's tendency to incorporate more iodine. As such, HA is expected to act more as a sink for iodine in the environment. In contrast, iodination of FA appeared to generate additional iodine binding sites, which resulted in greater iodine uptake capability and enhanced mobility (inferred from decreases in aliphatic compounds, increases in carbohydrates, and thus decreases in molecular hydrophobicity). These results indicate that certain NOM moieties may enhance while others may inhibit radioiodine mobility in the aqueous environment.

Structural characterization using 2D NMR spectroscopy and TMAH-GC × GC-MS: Application to humic acids from soils of an integrated agricultural system and an Atlantic native forest

Understanding the chemical make-up of soils and their structure is critical for constraining the role of soil organic matter (SOM) into the global biogeochemical cycles, as well as to understand the capability of SOM to sequester carbon and mitigate greenhouse gas emissions. Here, we use two-dimensional 1H-13C heteronuclear single quantum coherence nuclear magnetic resonance (2D 1H-13C HSQC NMR) spectroscopy to structurally characterize the most refractory component of SOM, the humic acid (HA). The observations from 2D 1H-13C HSQC NMR were coupled with lignin phenol and fatty acid measurements using tetramethylammonium hydroxide (TMAH) thermochemolysis – two-dimensional gas chromatography – mass spectrometry (TMAH-GC × GC-MS). We studied humic acids extracted from an integrated Crop – Livestock – Forest System (CLFS) agricultural area and an undisturbed Atlantic Native Forest (NF) area. We evaluated soils from two different depths: the topsoil (0–20 cm) and subsoil (60–100 cm) layers, and reveal the presence of oxidized ligninaceous phenols as we had previously hypothesized. Collectively, our results indicate that there are significant oxidative processes with increasing soil depth which are more pronounced in the CLFS relative to the NF area. Degradation of stearic acid with increasing depth in the CLFS soils indicated that the CLFS is more microbiologically active than NF. Therefore, CLFS is less biochemically stable than we originally perceived. The enhanced bio-reactivity of CLFS is likely driving the enhanced carbon sequestration in the CLFS soils. This is perhaps due to the diversity of biomass remnants available at the CLFS soil rhizosphere which allows for more different types of biomass to be sequestered as oxidized ligninaceous phenols. Our results employing structural characterization with 2D 1H-13C HSQC NMR and TMAH-GC × GC-MS provide a new layer of knowledge about the practice of integrated agricultural systems and allow us to understand the structure and fate of sequestered carbon in soils from different soil environments.

Composition and Protein Precipitation Capacity of Condensed Tannins in Purple Prairie Clover (Dalea purpurea Vent.).

This study aimed to determine the concentration and composition of condensed tannins (CT) in different tissues of purple prairie clover (PPC; Dalea purpurea Vent.) at different maturities and to determine their protein-precipitating capacity. The compositions of CT were elucidated after thiolysis with benzyl mercaptan followed by high-performance liquid-chromatography (HPLC) and 1H–13C heteronuclear single quantum coherence (HSQC) NMR spectroscopy. The results indicated that PPC flowering heads contained the highest CT concentration. Purple prairie clover CT consisted mainly of epicatechin (EC) and epigallocatechin (EGC) subunits. CT in the leaves were composed of more EC and less EGC than CT in stems and flowering heads at both the early flowering (EF) and late flowering (LF) head stages. The mean degree of polymerization was the highest for CT in stems and increased with maturity. CT isolated from PPC leaves at the early flowering head stage exhibited the greatest biological activity in terms of protein precipitation. Overall, the CT in PPC were predominantly procyanidins and the concentration and composition varied among the plant tissues and with maturity.

Nuclear Magnetic Resonance (NMR)-Based Quantification on Flavor-Active and Bioactive Compounds and Application for Distinguishment of Chicken Breeds.

The purpose of this study was to use 1H nuclear magnetic resonance (1H NMR) to quantify taste-active and bioactive compounds in chicken breasts and thighs from Korean native chicken (KNC) [newly developed KNCs (KNC-A, -C, and -D) and commercial KNC-H] and white-semi broiler (WSB) used in Samgye. Further, each breed was differentiated using multivariate analyses, including a machine learning algorithm designed to use metabolic information from each type of chicken obtained using 1H-13C heteronuclear single quantum coherence (2D NMR). Breast meat from KNC-D chickens were superior to those of conventional KNC-H and WSB chickens in terms of both taste-active and bioactive compounds. In the multivariate analysis, meat portions (breast and thigh) and chicken breeds (KNCs and WSB) could be clearly distinguished based on the outcomes of the principal component analysis and partial least square-discriminant analysis (R2=0.945; Q2=0.901). Based on this, we determined the receiver operating characteristic (ROC) curve for each of these components. AUC analysis identified 10 features which could be consistently applied to distinguish between all KNCs and WSB chickens in both breast (0.988) and thigh (1.000) meat without error. Here, both 1H NMR and 2D NMR could successfully quantify various target metabolites which could be used to distinguish between different chicken breeds based on their metabolic profile.