Liquid seaweed extracts identified using <Superscript>1</Superscript>H NMR profiles

Aqueous extracts of Ascophyllum nodosum and several other brown seaweeds are manufactured commercially and widely distributed for use on agricultural crops. The increasingly regulated international trade in such products requires that they be standardized and defined to a degree not previously required. We examined commercially available extracts using quantitative ¹H NMR and principal components analysis (PCA) techniques. Extracts manufactured over a 4-year period using the same process exhibited characteristic profiles that, on PCA, clustered as a discrete group distinct from the other commercial products examined. In addition to recognizing extracts made from different seaweeds, analysis of the ¹H spectra in the 0.35–4.70 ppm region allowed us to distinguish amongst extracts produced from the same algal species by different manufacturers. This result established that the process used to make an extract is an important variable in defining its composition. A comparison of the ¹H NMR integrals for the regions 1.0–3.0 ppm and 3.0–4.38 ppm revealed small but significant changes in the A. nodosum spectra that we attribute to seasonal variation in gross composition of the harvested seaweed. Such changes are reflected in the PCA scores plots and contribute to the scatter observed within the data point cluster observed for Acadian soluble extracts when all data are pooled. Quantitative analysis using ¹H NMR (qNMR) with a certified external standard (caffeine) showed a linear relationship with extract concentration over at least an order of magnitude (2.5–33 mg/mL; R ² > 0.97) for both spectral regions integrated. We conclude that qNMR can be used to profile (or “fingerprint”) commercial seaweed extracts and to quantify the amount of extract present relative to a suitably chosen standard. Issued as NRCC no. 42,652.     

<Superscript>1</Superscript>H NMR and GC-MS metabolic fingerprinting of developmental stages of <Emphasis Type="Italic">Rhizoctonia solani</Emphasis> sclerotia

Rhizoctonia solani AG-3 is a soilborne plant pathogen that forms resting vegetative structures called sclerotia. These compact structures are crucial to the pathogen’s survival and pathogenesis. The metabolic changes occurring during sclerotia development were monitored using proton nuclear magnetic resonance (¹H NMR) spectroscopy and gas chromatography–mass spectrometry (GC-MS). The validation, discrimination, and the establishment of correlative relationships between metabolite signals were performed by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The results of the analyses suggested that out of the 116 compounds that were simultaneously analyzed and compared using GC-MS, α-α-trehalose, d-glucose, 9-(Z)-octadecenoic and 9,12-octadecadienoic acids, xylitol, and glucitol were key metabolites that were highly dependent on the developmental stage of the sclerotia contributing to their discrimination and classification. Furthermore, the application of ¹H NMR and GC-MS metabolic fingerprinting on the same biological sample provided complementary information illustrating the value of this integrated approach in the study of metabolic changes in fungal structures.     

<Superscript>1</Superscript>H NMR metabolic profiling of gastric cancer patients with lymph node metastasis

Introduction

Gastric cancer (GC) is a malignant tumor worldwide. As primary pathway for metastasis, the lymphatic system is an important prognostic factor for GC patients. Although the metabolic changes of gastric cancer have been investigated in extensive studies, little effort focused on the metabolic profiling of lymph node metastasis (LNM)-positive or negative GC patients.

Objectives

We performed ¹H NMR spectrum of GC tissue samples with and without LNM to identify novel potential metabolic biomarkers in the process of LNM of GC.

Methods

¹H NMR-based untargeted metabolomics approach combined with multivariate statistical analyses were used to study the metabolic profiling of tissue samples from LNM-positive GC patients (n?=?40), LNM-negative GC patients (n?=?40) and normal controls (n?=?40).

Results

There was a clear separation between GC patients and normal controls, and 33 differential metabolites were identified in the study. Moreover, GC patients were also well-classified according to LNM-positive or negative. Totally eight distinguishing metabolites were selected in the metabolic profiling of GC patients with LNM-positive or negative, suggesting the metabolic dysfunction in the process of LNM. According to further validation and analysis, especially BCAAs metabolism (leucine, isoleucine, valine), GSH and betaine may be as potential factors of diagnose and prognosis of GC patients with or without LNM.

Conclusion

To our knowledge, this is the first metabolomics study focusing on LNM of GC. The identified distinguishing metabolites showed a promising application on clinical diagnose and therapy prediction, and understanding the mechanism underlying the carcinogenesis, invasion and metastasis of GC.

     

Detection of adulteration in Iranian saffron samples by <Superscript>1</Superscript>H NMR spectroscopy and multivariate data analysis techniques

Introduction

The high market value of saffron (Crocus sativus L.) has made it an attractive candidate for adulteration. Safflower (Carthamus tinctorius L.) and tartrazine are among the most common herbal and synthetic foreign materials that may be added to pure saffron for the purpose of adulteration. In spite of encouraging advances achieved in the identification of adulteration in saffron samples, the lack of a simple method with sufficient power for discrimination of pure high grade saffron from meticulously adulterated saffron samples persuaded us to perform this study.

Objectives

In this work, we show that ¹H NMR spectroscopy together with chemometric multivariate data analysis methods can be used for the detection of adulteration in saffron.

Methods

Authentic Iranian saffron samples (n?=?20) and adulterated samples that were prepared by adding either different quantities of natural plant materials such as safflower, or synthetic dyes such as tartrazine or naphthol yellow to pure saffron (n?=?22) composed the training set. This training set was used to build multivariate Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) models. The predictive power of the PLS-DA model was validated by testing the model against an external dataset (n?=?13).

Results

PCA and PLS-DA models could both discriminate between the authentic and adulterated samples, and the external validation showed 100% sensitivity and specificity for predicting the authenticity of suspicious samples. Peaks specific to authentic and adulterated samples were also characterized. Proximity of samples with unknown adulteration status to the samples adulterated with known compounds in the PCA provided insight regarding the identity of the adulterant in the suspicious samples. Furthermore, the authentic samples could be distinguished based on their cultivation site.

Conclusion

The present study demonstrates that the application of ¹H NMR spectroscopy coupled with multivariate data analysis is a suitable approach for detection of adulteration in saffron specimens. Outstanding sensitivity and specificity of the PLS-DA model in discriminating the authentic from adulterated samples in external validation confirmed the high predictive power of the model. The advantage of the present method is its power for detecting a wide spectrum of adulterants, ranging from synthetic dyes to herbal materials, in a single assay.

     

Comprehensive <Superscript>1</Superscript>H NMR metabolic profiling of body fluids for differentiation of meningitis in adults

Introduction

Meningitis, a morbidly infectious central nervous system pathology is accompanied by acute inflammation of the meninges, causing raised intracranial pressure linked with serious neurological sequelae.

Objective

To observe the variation in the metabolic profile, that may occur in serum and urine along with CSF in adults using ¹H NMR spectroscopy, with an attempt of appropriate and timely treatment regimen.

Methods

The ¹H NMR-based metabolomics has been performed in 115 adult subjects for differentiating bacterial meningitis (BM) and tubercular meningitis (TBM).

Results

The discriminant function analysis (DFA) of the three bio-fluids collectively identified 3-hydroxyisovalerate, lactate, glucose, formate, valine, alanine, ketonic bodies, malonate and choline containing compounds (choline and GPC) as significant metabolites among cases versus control group. The differentiation of bacterial meningitis and tuberculous meningitis (BM vs. TBM) can be done on the basis of identification of 3-hydroxyisovalerate, isobutyrate and formate in case of CSF (with a correct classification of 78 %), alanine in serum (correct classification 60 %), valine and acetone in case of urine (correct classification 89.1 %). The NMR spectral bins based orthogonal signal correction principal component analysis score plots of significant metabolites obtained from DFA also provided group classification among cases versus control group in CSF, serum and urine samples. The variable importance in projection scores also identified similar significant metabolites as obtained from DFA, collectively in CSF, serum and urine samples, responsible for differentiation of meningitis.

Conclusion

The CSF contained metabolites which are formed during infection and inflammation, and these were also found in significant quantity in serum and urine samples.

     

Automated amino acid side-chain NMR assignment of proteins using <Superscript>13</Superscript>C- and <Superscript>15</Superscript>N-resolved 3D [<Superscript>1</Superscript>H,<Superscript>1</Superscript>H]-NOESY

ASCAN is a new algorithm for automatic sequence-specific NMR assignment of amino acid side-chains in proteins, which uses as input the primary structure of the protein, chemical shift lists of (1)H(N), (15)N, (13)C(alpha), (13)C(beta) and possibly (1)H(alpha) from the previous polypeptide backbone assignment, and one or several 3D (13)C- or (15)N-resolved [(1)H,(1)H]-NOESY spectra. ASCAN has also been laid out for the use of TOCSY-type data sets as supplementary input. The program assigns new resonances based on comparison of the NMR signals expected from the chemical structure with the experimentally observed NOESY peak patterns. The core parts of the algorithm are a procedure for generating expected peak positions, which is based on variable combinations of assigned and unassigned resonances that arise for the different amino acid types during the assignment procedure, and a corresponding set of acceptance criteria for assignments based on the NMR experiments used. Expected patterns of NOESY cross peaks involving unassigned resonances are generated using the list of previously assigned resonances, and tentative chemical shift values for the unassigned signals taken from the BMRB statistics for globular proteins. Use of this approach with the 101-amino acid residue protein FimD(25-125) resulted in 84% of the hydrogen atoms and their covalently bound heavy atoms being assigned with a correctness rate of 90%. Use of these side-chain assignments as input for automated NOE assignment and structure calculation with the ATNOS/CANDID/DYANA program suite yielded structure bundles of comparable quality, in terms of precision and accuracy of the atomic coordinates, as those of a reference structure determined with interactive assignment procedures. A rationale for the high quality of the ASCAN-based structure determination results from an analysis of the distribution of the assigned side chains, which revealed near-complete assignments in the core of the protein, with most of the incompletely assigned residues located at or near the protein surface.     

Automated metabolite identification from biological fluid <Superscript>1</Superscript>H NMR spectra

Introduction

Metabolite identification in biological samples using Nuclear Magnetic Resonance (NMR) spectra is a challenging task due to the complexity of the biological matrices.

Objectives

This paper introduces a new, automated computational scheme for the identification of metabolites in 1D ¹H NMR spectra based on the Human Metabolome Database.

Methods

The methodological scheme comprises of the sequential application of preprocessing, data reduction, metabolite screening and combination selection.

Results

The proposed scheme has been tested on the 1D ¹H NMR spectra of: (a) an amino acid mixture, (b) a serum sample spiked with the amino acid mixture, (c) 20 blood serum, (d) 20 human amniotic fluid samples, (e) 160 serum samples from publicly available database. The methodological scheme was compared against widely used software tools, exhibiting good performance in terms of correct assignment of the metabolites.

Conclusions

This new robust scheme accomplishes to automatically identify peak resonances in ¹H-NMR spectra with high accuracy and less human intervention with a wide range of applications in metabolic profiling.

     

Genetic discrimination between<Emphasis Type="Italic">Catharanthus roseus</Emphasis> cultivars by metabolic fingerprinting using<Superscript>1</Superscript>H NMR spectra of aromatic compounds

When whole cell extracts are subjected to proton nuclear magnetic resonance spectroscopy (¹H NMR), metabolite profiles are generated that contain overlapping signals of the majority of compounds within the extract. In order to determine whether pattern recognition based on the metabolite profiles of higher plants is able to genetically discriminate between plants, we analyzed leaf samples of eight cultivars ofCatharanthus roseus by¹H NMR. Hierarchical dendrograms, based on the principal component analysis of the¹H NMR total, aliphatic carbohydrate and aromatic region data, revealed possible relationships between the cultivars. The dendrogram based on the aromatic region data was in general agreement with the genetic relationships determined by conventional DNA fingerprinting methods. Secologanin and polyphenols were assigned to the signals of the¹H NMR spectra, and contributed most profoundly to the discrimination between cultivars. The overall results indicate that the genetic relationships betweenC. roseus cultivars are reflected in the differences of the aromatic compounds in the leaves.     

<Superscript>1</Superscript>H, <Superscript>15</Superscript>N and <Superscript>13</Superscript>C assignment of the amyloidogenic protein medin using fast-pulsing NMR techniques

Thirty-one proteins are known to form extracellular fibrillar amyloid in humans. Molecular information about many of these proteins in their monomeric, intermediate or fibrillar form and how they aggregate and interact to form the insoluble fibrils is sparse. This is because amyloid proteins are notoriously difficult to study in their soluble forms, due to their inherent propensity to aggregate. Using recent developments in fast NMR techniques, band-selective excitation short transient and band-selective optimized flip-angle short-transient heteronuclear multiple quantum coherence we have been able to assign a 5 kDa full-length amyloidogenic protein called medin. Medin is the key protein component of the most common form of localised amyloid with a proposed role in aortic aneurysm and dissection. This assignment will now enable the study of the early interactions that could influence initiation and progression of medin aggregation. The chemical shifts have been deposited in the BioMagRes-Bank accession Nos. 25399 and 26576.     

Quantitative determination of metabolites in human urine by <Superscript>1</Superscript>H NMR spectroscopy

Conditions for registration of urinary ¹H NMR spectra have been optimized in order to achieve maximal accuracy of quantitative analysis. Urinary samples from patients with acute pancreatitis have been investigated and spectral data of identified urinary metabolites and results of their quantitative determination are given. Employment of ¹H NMR spectra is perspective for the development of new laboratory diagnostic methods.     

<Superscript>1</Superscript>H, <Superscript>13</Superscript>C and <Superscript>15</Superscript>N backbone NMR chemical shift assignments of the C-terminal P4 domain of Ahnak

Ahnak is a ~?700 kDa polypeptide that was originally identified as a tumour-related nuclear phosphoprotein, but later recognized to play a variety of diverse physiological roles related to cell architecture and migration. A critical function of Ahnak is modulation of Ca²⁺ signaling in cardiomyocytes by interacting with the β subunit of the L-type Ca²⁺ channel (Ca_V1.2). Previous studies have identified the C-terminal region of Ahnak, designated as P3 and P4 domains, as a key mediator of its functional activity. We report here the nearly complete ¹H, ¹³C and ¹⁵N backbone NMR chemical shift assignments of the 11 kDa C-terminal P4 domain of Ahnak. This study lays the foundations for future investigations of functional dynamics, structure determination and interaction site mapping of the Ca_V1.2-Ahnak complex.     

Critical test of some computational methods for prediction of NMR <Superscript>1</Superscript>H and <Superscript>13</Superscript>C chemical shifts

Performance of 18 DFT functionals (B1B95, B3LYP, B3PW91, B97D, BHandHLYP, BMK, CAM-B3LYP, HSEh1PBE, M06-L, mPW1PW91, O3LYP, OLYP, OPBE, PBE1PBE, tHCTHhyb, TPSSh, wB97xD, VSXC) in combinations with six basis sets (cc-pVDZ, aug-cc-pVDZ, cc-pVTZ, aug-cc-pVTZ, IGLO-II, and IGLO-III) and three methods for calculating magnetic shieldings (GIAO, CSGT, IGAIM) was tested for predicting ¹H and ¹³C chemical shifts for 25 organic compounds, for altogether 86 H and 88 C atoms. Proton shifts varied between 1.03 ppm to 12.00 ppm and carbon shifts between 7.87 ppm to 209.28 ppm. It was found that the best method for calculating ¹³C shifts is PBE1PBE/aug-cc-pVDZ with CSGT or IGAIM approaches (mae?=?1.66 ppm), for ¹H the best results were obtained with HSEh1PBE, mPW1PW91, PBE1PBE, CAM-B3LYP, and B3PW91 functionals with cc-pVTZ basis set and with CSGT or IGAIM approaches (mae?=?0.28 ppm). We found that often larger basis sets do not give better results for chemical shifts. The best basis sets for calculating ¹H and ¹³C chemical shifts were cc-pVTZ and aug-cc-pVDZ, respectively. CSGT and IGAIM NMR approaches can perform really well and are in most cases better than popular GIAO approach.

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Graphical Abstract Mean absolute errors for ¹H and ¹³C chemical shifts and computational times of neutral toluene molecule with aug-cc-pVDZ basis set and CSGT approach

     

<Superscript>1</Superscript>H, <Superscript>13</Superscript>C and <Superscript>15</Superscript>N chemical shift assignments of Saccharomyces cerevisiae type 1 thioredoxin in the oxidized state by solution NMR spectroscopy

Thioredoxins (Trx) are ubiquitous proteins that regulate several biochemical processes inside the cell. Trx is an important player, displaying oxidoreductase activity and helping to keep and regulate the oxidative state of the cellular environment. Trx also participates in the regulation of many cellular functions, such as DNA synthesis, protection against oxidative stress, cell cycle and signal transduction. The oxidized Trx is the target for another set of proteins, such as thioredoxin reductase (TrR), which used the reductive potential of NADPH. The oxidized state of Trx also plays important role in regulation of redox state in the cells. In this regard, the oxidized form of Trx is a putative conformer that contributes to the cellular redox environment. Here we report the chemical shift assignments (¹H, ¹³C and ¹⁵N) in solution at 15 °C. We also showed the secondary structure analysis of the oxidized form of yeast thioredoxin (yTrx1) as basis for future NMR studies of protein–target interactions and dynamics. The assignment was done at low concentration (200 µM) because it is important to keep intact the water cavity.     

Nitrogen metabolism of asparagine and glutamate in Vero cells studied by <Superscript>1</Superscript>H/<Superscript>15</Superscript>N NMR spectroscopy

Glutamine-free culture of Vero cells has previously been shown to cause higher cell yield and lower ammonia accumulation than that in glutamine-containing culture. Nitrogen metabolism of asparagine and glutamate as glutamine replacer was studied here using nuclear magnetic resonance (NMR) spectroscopy. ¹⁵N-labelled glutamate or asparagine was added and their incorporation into nitrogenous metabolites was monitored by heteronuclear multiple bond coherence (HMBC) NMR spectroscopy. In cells incubated with l-[¹⁵N]glutamate, the ¹⁵N label was subsequently found in a number of metabolites including alanine, aspartate, proline, and an unidentified compound. No detectable signal occurred, indicating that glutamate was utilized by transamination rather than by oxidative deamination. In cells incubated with l-[2-¹⁵N]asparagine, the ¹⁵N label was subsequently found in aspartate, the amine group of glutamate/glutamine, and in two unidentified compounds. Incubation of cells with l-[4-¹⁵N]asparagine showed that the amide nitrogen of asparagine was predominantly transferred to glutamine amide. There was no detectable production of , showing that most of the asparagine amide was transaminated by asparagine synthetase rather than deaminated by asparaginase. Comparing with a glutamine-containing culture, the activities of phosphate-activated glutaminase (PAG), glutamate dehydrogenase (GDH) and alanine aminotransferase (ALT) decreased significantly and the activity of aspartate aminotransferase (AST) decreased slightly.     

<Superscript>1</Superscript>H, <Superscript>13</Superscript>C and <Superscript>15</Superscript>N NMR assignments of a calcium-binding protein from <Emphasis Type="Italic">Entamoeba histolytica</Emphasis>

We report almost complete sequence specific ¹H, ¹³C and ¹⁵N NMR assignments of a 150-residue long calmodulin-like calcium-binding protein from Entamoeba histolytica (EhCaBP6), as a prelude to its structural and functional characterization.     

<Superscript>1</Superscript>H, <Superscript>15</Superscript>N, <Superscript>13</Superscript>C resonance assignment of human osteopontin

Osteopontin (OPN) is a 33.7 kDa intrinsically disordered protein and a member of the SIBLING family of proteins. OPN is bearing a signal peptide for secretion into the extracellular space, where it exerts its main physiological function, the control of calcium biomineralization. It is often involved in tumorigenic processes influencing proliferation, migration and survival, as well as the adhesive properties of cancer cells via CD44 and integrin signaling pathways. Here we report the nearly complete NMR chemical shift assignment of recombinant human osteopontin.     

Quantitative <Superscript>1</Superscript>H NMR metabolomics reveals extensive metabolic reprogramming of primary and secondary metabolism in elicitor-treated opium poppy cell cultures

Background  

Opium poppy (Papaver somniferum) produces a diverse array of bioactive benzylisoquinoline alkaloids and has emerged as a model system to study plant alkaloid metabolism. The plant is cultivated as the only commercial source of the narcotic analgesics morphine and codeine, but also produces many other alkaloids including the antimicrobial agent sanguinarine. Modulations in plant secondary metabolism as a result of environmental perturbations are often associated with the altered regulation of other metabolic pathways. As a key component of our functional genomics platform for opium poppy we have used proton nuclear magnetic resonance (¹H NMR) metabolomics to investigate the interplay between primary and secondary metabolism in cultured opium poppy cells treated with a fungal elicitor.     

A multidimensional <Superscript>1</Superscript>H NMR lipidomics workflow to address chemical food safety issues

Introduction

Although it is still at a very early stage compared to its mass spectrometry (MS) counterpart, proton nuclear magnetic resonance (NMR) lipidomics is worth being investigated as an original and complementary solution for lipidomics. Dedicated sample preparation protocols and adapted data acquisition methods have to be developed to set up an NMR lipidomics workflow; in particular, the considerable overlap observed for lipid signals on 1D spectra may hamper its applicability.

Objectives

The study describes the development of a complete proton NMR lipidomics workflow for application to serum fingerprinting. It includes the assessment of fast 2D NMR strategies, which, besides reducing signal overlap by spreading the signals along a second dimension, offer compatibility with the high-throughput requirements of food quality characterization.

Method

The robustness of the developed sample preparation protocol is assessed in terms of repeatability and ability to provide informative fingerprints; further, different NMR acquisition schemes—including classical 1D, fast 2D based on non-uniform sampling or ultrafast schemes—are evaluated and compared. Finally, as a proof of concept, the developed workflow is applied to characterize lipid profiles disruption in serum from β-agonists diet fed pigs.

Results

Our results show the ability of the workflow to discriminate efficiently sample groups based on their lipidic profile, while using fast 2D NMR methods in an automated acquisition framework.

Conclusion

This work demonstrates the potential of fast multidimensional ¹H NMR—suited with an appropriate sample preparation—for lipidomics fingerprinting as well as its applicability to address chemical food safety issues.