Identification of metabolites from 2D (1)H-(13)C HSQC NMR using peak correlation plots

Richer than previously probed: An application of 1H NMR reveals one hundred metabolites using only fifty microliter serum

The classical approach restricts the detection of metabolites in serum samples by using nuclear magnetic resonance (NMR) spectroscopy; however, the presence of copious proteins and lipoproteins emphasize and necessitate the development of a contemporary, high-throughput tactic. To eliminate the lipoproteins and proteins from sera to engender filtered sera (FS), the study was executed with 50 μl serum obtained from five healthy individuals with 5 years of age difference from 25 to 45 years old and the application of a unique mechanical filter with molecular weight cut-off value of 2KDa. The 10 μl FS from each individual was pooled to make 50 μl final volume filled in a co-axial tube for acquisition of a battery of 1D/2D investigations at 800 MHz NMR spectrometer and the assigned metabolites was confirmed through mass spectrometry as well as by comparing 1H NMR spectra of individual metabolites. This innovative tactic is commissioning to reveal more than 100 metabolites. In contrast to the protein precipitation method, 24 new metabolites were recognized in the present study. The present innovative approach characterizes nucleosides, nitrogenous bases, and volatile metabolites that possibly produce a landmark for the delineation of a comprehensive metabolic profile applicable for detection of the molecular cause of pathogenicity, early-stage disease detection and prognosis, inborn error of metabolism, and forensic investigations exerting the least volume of FS and NMR spectroscopy. The assignment of 100 metabolites using 1H NMR-based FS is described for the first time in the present report.

Xylan glucuronic acid side chains fix suberin-like aliphatic compounds to wood cell walls

Wood is the most important repository of assimilated carbon in the biosphere, in the form of large polymers (cellulose, hemicelluloses including glucuronoxylan, and lignin) that interactively form a composite, together with soluble extractives including phenolic and aliphatic compounds. Molecular interactions among these compounds are not fully understood. We have targeted the expression of a fungal α-glucuronidase to the wood cell wall of aspen (Populus tremula L. × tremuloides Michx.) and Arabidopsis (Arabidopsis thaliana (L.) Heynh), to decrease contents of the 4-O-methyl glucuronopyranose acid (mGlcA) substituent of xylan, to elucidate mGlcA's functions. The enzyme affected the content of aliphatic insoluble cell wall components having composition similar to suberin, which required mGlcA for binding to cell walls. Such suberin-like compounds have been previously identified in decayed wood, but here, we show their presence in healthy wood of both hardwood and softwood species. By contrast, γ-ester bonds between mGlcA and lignin were insensitive to cell wall-localized α-glucuronidase, supporting the intracellular formation of these bonds. These findings challenge the current view of the wood cell wall composition and reveal a novel function of mGlcA substituent of xylan in fastening of suberin-like compounds to cell wall. They also suggest an intracellular initiation of lignin-carbohydrate complex assembly.

Urine NMR Metabolomics for Precision Oncology in Colorectal Cancer

Metabolomics is a fundamental approach to discovering novel biomarkers and their potential use for precision medicine. When applied for population screening, NMR-based metabolomics can become a powerful clinical tool in precision oncology. Urine tests can be more widely accepted due to their intrinsic non-invasiveness. Our review provides the first exhaustive evaluation of NMR metabolomics for the determination of colorectal cancer (CRC) in urine. A specific search in PubMed, Web of Science, and Scopus was performed, and 10 studies met the required criteria. There were no restrictions on the query for study type, leading to not only colorectal cancer samples versus control comparisons, but also prospective studies of surgical effects. With this review, all compounds in the included studies were merged into a database. In doing so, we identified up to 100 compounds in urine samples, and 11 were found in at least three articles. Results were analyzed in three groups: case (CRC and adenomas)/control, pre-/post-surgery, and combining both groups. When combining the case-control and the pre-/post-surgery groups, up to twelve compounds were found to be relevant. Seven down-regulated metabolites in CRC were identified, creatinine, 4-hydroxybenzoic acid, acetone, carnitine, d-glucose, hippuric acid, l-lysine, l-threonine, and pyruvic acid, and three up-regulated compounds in CRC were identified, acetic acid, phenylacetylglutamine, and urea. The pathways and enrichment analysis returned only two pathways significantly expressed: the pyruvate metabolism and the glycolysis/gluconeogenesis pathway. In both cases, only the pyruvic acid (down-regulated in urine of CRC patients, with cancer cell proliferation effect in the tissue) and acetic acid (up-regulated in urine of CRC patients, with chemoprotective effect) were present.

Novel Metabolic Signatures of Prostate Cancer Revealed by 1H-NMR Metabolomics of Urine

Prostate cancer (PC) is one of the most common male cancers worldwide. Until now, there is no consensus about using urinary metabolomic profiling as novel biomarkers to identify PC. In this study, urine samples from 50 PC patients and 50 non-cancerous individuals (control group) were collected. Based on 1H nuclear magnetic resonance (1H-NMR) analysis, 20 metabolites were identified. Subsequently, principal component analysis (PCA), partial least squares-differential analysis (PLS-DA) and ortho-PLS-DA (OPLS-DA) were applied to find metabolites to distinguish PC from the control group. Furthermore, Wilcoxon test was used to find significant differences between the two groups in metabolite urine levels. Guanidinoacetate, phenylacetylglycine, and glycine were significantly increased in PC, while L-lactate and L-alanine were significantly decreased. The receiver operating characteristics (ROC) analysis revealed that the combination of guanidinoacetate, phenylacetylglycine, and glycine was able to accurately differentiate 77% of the PC patients with sensitivity = 80% and a specificity = 64%. In addition, those three metabolites showed significant differences in patients stratified for Gleason score 6 and Gleason score ≥7, indicating potential use to detect significant prostate cancer. Pathway enrichment analysis using the KEGG (Kyoto Encyclopedia of Genes and Genomes) and the SMPDB (The Small Molecule Pathway Database) revealed potential involvement of KEGG "Glycine, Serine, and Threonine metabolism" in PC. The present study highlights that guanidinoacetate, phenylacetylglycine, and glycine are potential candidate biomarkers of PC. To the best knowledge of the authors, this is the first study identifying guanidinoacetate, and phenylacetylglycine as potential novel biomarkers in PC.

Consecutive Queries to Assess Biological Correlation in NMR Metabolomics: Performance of Comprehensive Search of Multiplets over Typical 1D 1H NMR Database Search

NMR-based metabolomics requires proper identification of metabolites to draw conclusions from the system under study. Normally, multivariate data analysis is performed using 1D ¹H NMR spectra, and identification of peaks (and then compounds) relevant to the classification is accomplished using database queries as a first step. 1D ¹H NMR spectra of complex mixtures often suffer from peak overlap. To overcome this issue, several studies employed the projections of the (tilted and symmetrized) 2D ¹H J-resolved (JRES) spectra, p-JRES, which are similar to 1D ¹H decoupled spectra. Nonetheless, there are no public databases available that allow searching for chemical shift spectral data for multiplets. We present the Chemical Shift Multiplet Database (CSMDB), built utilizing JRES spectra obtained from the Birmingham Metabolite Library. The CSMDB provides scoring accounting for both matched and unmatched peaks from a query list and the database hits. This input list is generated from a projection of a 2D statistical correlation analysis on the JRES spectra, p-(JRES-STOCSY), being able to compare the multiplets for the matched peaks, in essence, the f1 traces from the JRES-STOCSY spectrum and from the database hit. The inspection of the unmatched peaks for the database hit allows the retrieval of peaks in the query list that have a decreased correlation coefficient due to low intensities. The CSMDB is coupled to "ConQuer ABC", which permits the assessment of biological correlation by means of consecutive queries with the unmatched peaks in the first and subsequent queries.

NMR and GC/MS analysis of industrial chloroparaffin mixtures

Chlorinated paraffins (CPs) are high-volume chemicals used worldwide in various industries as plasticizers, lubricants, and flame retardants. CPs are produced by chlorination of alkane mixtures which leads to complex products of thousands of homologs and congeners. Classic mass spectrometric analyses of CPs allow determining carbon chain lengths and degrees of chlorination while information on the substitution patterns cannot be derived. Therefore, we performed different one- and two-dimensional nuclear magnetic resonance spectroscopy (NMR) experiments, elemental analysis (EA), and gas chromatography coupled with electron capture negative ion mass spectrometry (GC/ECNI-MS) for the analysis of ten technical CP products with 42%, 52%, and 70% chlorine content from four producers. Slight differences in chlorine content but varying chain length compositions were observed for similarly labeled products from different manufacturers. Two-dimensional heteronuclear spectral quantum coherence (HSQC) measurements helped to evaluate ten structural elements in the products and confirmed the presence of geminal chlorine atoms in primary and secondary carbons in products with 70% chlorine. The variation of signal groups increased with increasing chlorine content of the products. Two-dimensional heteronuclear multiple bond coherence (HMBC) analysis of one sample and GC/ECNI-MS measurements indicated the presence of impurities (e.g., C₉-CPs, iso-alkanes) in different technical CP products. These methods could in future allow for better distinction of CP mixtures, and an improved trace-back of environmental CPs to the source, based on specific structural features. Additionally, further structural characterization could help in the development of more accurate analysis processes. Graphical Abstract.

Tackling the Peak Overlap Issue in NMR Metabolomics Studies: 1D Projected Correlation Traces from Statistical Correlation Analysis on Nontilted 2D 1H NMR J-Resolved Spectra

The identification of metabolites in complex biological matrices is a challenging task in 1D ¹H-NMR-based metabolomics studies. Statistical total correlation spectroscopy (STOCSY) has emerged for aiding the structural elucidation by revealing the peaks that present a high correlation to a driver peak of interest (which would likely belong to the same molecule). However, in these studies, the signals from metabolites are normally present as a mixture of overlapping resonances, limiting the performance of STOCSY. As an alternative to avoid the overlap issue, 2D ¹H homonuclear J-resolved (JRES) spectra were projected, in their usual tilted and symmetrized processed form, and STOCSY was applied on these 1D projections (p-JRES-STOCSY). Nonetheless, this approach suffers in cases where the signals are very close. In addition, STOCSY was applied to the whole JRES spectra (also tilted) to identify correlated multiplets, although the overlap issue in itself was not addressed directly and the subsequent search in databases is complicated in cases of higher order coupling. With these limitations in mind, in the present work, we propose a new methodology based on the application of STOCSY on a set of nontilted JRES spectra, detecting peaks that would overlap in 1D spectra of the same sample set. Correlation comparison analysis for peak overlap detection (COCOA-POD) is able to reconstruct projected 1D STOCSY traces that result in more suitable database queries, as all peaks are summed at their f2 resonances instead of the resonance corresponding to the multiplet center in the tilted JRES spectra. (The peak dispersion and resolution enhancement gained are not sacrificed by the projection.) Besides improving database queries with better peak lists obtained from the projections of the 2D STOCSY analysis, the overlap region is examined, and the multiplet itself is analyzed from the correlation trace at 45° to obtain a cleaner multiplet profile, free from contributions from uncorrelated neighboring peaks.

Identification of antiplasmodial triterpenes from Keetia species using NMR-based metabolic profiling

INTRODUCTION

The increase in multidrug resistance and lack of efficacy in malaria therapy has propelled the urgent discovery of new antiplasmodial drugs, reviving the screening of secondary metabolites from traditional medicine. In plant metabolomics, NMR-based strategies are considered a golden method providing both a holistic view of the chemical profiles and a correlation between the metabolome and bioactivity, becoming a corner stone of drug development from natural products.

OBJECTIVE

Create a multivariate model to identify antiplasmodial metabolites from ¹H NMR data of two African medicinal plants, Keetia leucantha and K. venosa.

METHODS

The extracts of twigs and leaves of Keetia species were measured by ¹H NMR and the spectra were submitted to orthogonal partial least squares (OPLS) for antiplasmodial correlation.

RESULTS

Unsupervised ¹H NMR analysis showed that the effect of tissues was higher than species and that triterpenoids signals were more associated to Keetia twigs than leaves. OPLS-DA based on Keetia species correlated triterpene signals to K. leucantha, exhibiting a higher concentration of triterpenoids and phenylpropanoid-conjugated triterpenes than K. venosa. In vitro antiplasmodial correlation by OPLS, validated for all Keetia samples, revealed that phenylpropanoid-conjugated triterpenes were highly correlated to the bioactivity, while the acyclic squalene was found as the major metabolite in low bioactivity samples.

CONCLUSION

NMR-based metabolomics combined with supervised multivariate data analysis is a powerful strategy for the identification of bioactive metabolites in plant extracts. Moreover, combination of statistical total correlation spectroscopy with 2D NMR allowed a detailed analysis of different triterpenes, overcoming the challenge posed by their structure similarity and coalescence in the aliphatic region.

NMR-Based Urinary Metabolomics Applications

The field of metabolomics has been growing tremendously over the recent years and, consistent with that growth, a number of investigators have been looking at the potential of NMR-based urinary metabolomics for several applications. While such applications have shown promising results, there still remains an enormous amount of work to be done before this approach becomes accepted and widely used in clinical diagnostics and other biomedical applications. To achieve such goals, optimization of parameters and standardization of protocols are of paramount importance. In view of this, in this chapter, we present some recommended methods and procedures that can help researchers in the field. Furthermore, we have highlighted some of the challenges encountered in such applications and suggested some possible ways to overcome those challenges.

Nuclear magnetic resonance spectroscopy as a new approach for improvement of early diagnosis and risk stratification of prostate cancer

Prostate cancer (PCa) is the second most common male cancer worldwide and the fifth leading cause of death from cancer in men. Early detection and risk stratification is the most effective way to improve the survival of PCa patients. Current PCa biomarkers lack sufficient sensitivity and specificity to cancer. Metabolite biomarkers are evolving as a new diagnostic tool. This review is aimed to evaluate the potential of metabolite biomarkers for early detection, risk assessment, and monitoring of PCa. Of the 154 identified publications, 27 and 38 were original papers on urine and serum metabolomics, respectively. Nuclear magnetic resonance (NMR) is a promising method for measuring concentrations of metabolites in complex samples with good reproducibility, high sensitivity, and simple sample processing. Especially urine-based NMR metabolomics has the potential to be a cost-efficient method for the early detection of PCa, risk stratification, and monitoring treatment efficacy.

Beyond the limit of assignment of metabolites using minimal serum samples and 1H NMR spectroscopy with cross-validation by mass spectrometry

Identification of NMR-based metabolic indexes is limited by the deleterious effects of copious proteins and lipoproteins in the serum that accentuate the need for advance and high-throughput method. We tried to explore the use of a novel filtration (2KDa molecular weight cut-off) approach to remove the proteins from serum following use of less sample volume (only 150 μL of filtered serum), combining an array of 1D/2D NMR experiments (at 800 MHz spectrometer), spiking experiments with standard compounds, and validated by mass spectrometry. This novel method enabled the identification of a large number (n = 73) of metabolites and their percentage of abundance in the present study cohort. Mass spectrometry further validates and confirms the presence of all these 73 metabolites using same filtered serum. This study reveals seven new metabolites (citrulline, inosine, taurine, trimethyl amine, methylmalonate, uracil, methanol) in filtered serum using 1D/2D NMR spectroscopy that were not observed in earlier available literature using protein precipitation approach. This novel method delineates volatile metabolites, nitrogenous bases and nucleosides that may provide a milestone for the identification of inborn error of metabolism, pathogenicity at molecular level, disease identification and prognosis, and forensic studies using minimal volume of filtered serum samples and NMR spectroscopy.

SOFAST-HMQC-an efficient tool for metabolomics

Nuclear magnetic resonance (NMR)-based metabolomics relies mostly on 1D NMR; however, the technique is limited by overlap of the signals from the metabolites. In order to circumvent this problem, 2D ¹H-¹³C correlation spectroscopy techniques are often used. However owing to poorer natural abundance and gyromagnetic ratio of ¹³C, the acquisition time for 2D ¹H-¹³C heteronuclear single quantum coherence spectroscopy (HSQC) is long. This makes it almost impossible to be used in high throughput study. We have reported the application of selective optimized flip angle short transient (SOFAST) technique coupled to heteronuclear multiple quantum correlation (HMQC) along with nonlinear sampling (NUS) in urine and serum samples. This technique takes sevenfold less experimental time than the conventional ¹H-¹³C HSQC experiment with retention of almost all molecular information. Hence, this can be used for high throughput study. Graphical abstract SOFAST-HMQC is a two-dimensional NMR technique that significantly decreases experimental time without loss of information. This technique is applied in complex biofluid samples that are used for high throughput metabolomics studies and shows promise of better information recovery than conventional two-dimensional NMR technique in shorter time.

NMR-based metabolomic analysis of wild, greenhouse, and in vitro regenerated shoots of Cymbopogon schoenanthus subsp. proximus with GC-MS assessment of proximadiol

Cymbopogon schoenanthus subsp. proximus is a wild plant distributed in subtropical and east Africa extending from the north to the southern parts of Egypt. Widely used in folk medicine, it is the source of the diuretic sesquiterpene proximadiol. Nuclear magnetic resonance metabolomic analysis of polar extracts of shoots from wild, greenhouse, somatic embryos, and direct and indirect organogenic in vitro cultures was carried out. Metabolic profiling yielded 39 compounds, of which common metabolites were 15 (38.4%). Unique metabolites were trehalose (2.5%) in the wild plants, 2-hydroxylisobutyrate, galactarate and tyrosine (7.6%) in indirect organogenic shoots. Tartrate was found only in direct regenerated shoots (2.5%). Metabolites identified in greenhouse and embryogenic shoots showed no unique compounds. Multivariate analysis revealed significant differences between all tested shoots. 4-aminobutyrate, alanine, glutamine, glucose, fructose, and sucrose were the most significantly different metabolites. Proximadiol was identified and quantitatively measured from the non-polar extract of different types of shoots using gas chromatography and mass spectrometry (GC-MS). Concentrations ranged from 3.6 ± 0.03 to 198.6 ± 7.2 µg/100 mg dry weight in regenerated shoots from somatic embryogenesis and in wild plant shoots, respectively. Direct organogenesis yielded the highest in vitro concentration (20.3 ± 0.5 µg/100 mg dry weight). This study reported the metabolic profiling of C. schoenanthus polar extract and identified primary metabolites that are unique to the wild type and shoots regenerated from different in vitro cultures. Proximadiol was quantified and the in vitro culture system yielding the highest concentration relative to the wild plant was identified.

Diagnostic Applications of Nuclear Magnetic Resonance-Based Urinary Metabolomics

Metabolomics is a rapidly growing field with potential applications in various disciplines. In particular, metabolomics has received special attention in the discovery of biomarkers and diagnostics. This is largely due to the fact that metabolomics provides critical information related to the downstream products of many cellular and metabolic processes which could provide a snapshot of the health/disease status of a particular tissue or organ. Many of these cellular products eventually find their way to urine; hence, analysis of urine via metabolomics has the potential to yield useful diagnostic and prognostic information. Although there are a number of analytical platforms that can be used for this purpose, this review article will focus on nuclear magnetic resonance-based metabolomics. Furthermore, although there have been many studies addressing different diseases and metabolic disorders, the focus of this review article will be in the following specific applications: urinary tract infection, kidney transplant rejection, diabetes, some types of cancer, and inborn errors of metabolism. A number of methodological considerations that need to be taken into account for the development of a clinically useful optimal test are discussed briefly.

13C Tracking after 13CO2 Supply Revealed Diurnal Patterns of Wood Formation in Aspen

Wood of trees is formed from carbon assimilated in the photosynthetic tissues. Determining the temporal dynamics of carbon assimilation, subsequent transport into developing wood, and incorporation to cell walls would further our understanding of wood formation in particular and tree growth in general. To investigate these questions, we designed a (13)CO2 labeling system to study carbon transport and incorporation to developing wood of hybrid aspen (Populus tremula × tremuloides). Tracking of (13)C incorporation to wood over a time course using nuclear magnetic resonance spectroscopy revealed diurnal patterns in wood cell wall biosynthesis. The dark period had a differential effect on (13)C incorporation to lignin and cell wall carbohydrates. No (13)C was incorporated into aromatic amino acids of cell wall proteins in the dark, suggesting that cell wall protein biosynthesis ceased during the night. The results show previously unrecognized temporal patterns in wood cell wall biosynthesis, suggest diurnal cycle as a possible cue in the regulation of carbon incorporation to wood, and establish a unique (13)C labeling method for the analysis of wood formation and secondary growth in trees.