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.     

Use of EDTA to minimize ionic strength dependent frequency shifts in the <Superscript>1</Superscript>H NMR spectra of urine

The ¹H NMR spectrum of urine exhibits a large number of detectable metabolites and is, therefore, highly suitable for the study of perturbations caused by disease, toxicity, nutrition or environmental factors in humans and animals. However, variations in the chemical shifts and intensities due to altered pH and ionic strength present a challenge in NMR-based studies. With a view towards understanding and minimizing the effects of these variations, we have extensively studied the effects of ionic strength and pH on the chemical shifts of common urine metabolites and their possible reduction using EDTA (ethylenediaminetetraacetic acid). ¹H NMR chemical shifts for alanine, citrate, creatinine, dimethylamine, glycine, histidine, hippurate, formate and the internal reference, TSP (trimethylsilylpropionic acid-d₄, sodium salt) obtained under different conditions were used to assess each effect individually. EDTA minimizes the frequency shifts of the metabolites that have a propensity for metal binding. Chelation of such metal ions is evident from the appearance of signals from EDTA complexed to divalent metal ions such as calcium and magnesium. Not surprisingly, increasing the buffer concentration or buffer volume also minimizes pH dependent frequency shifts. The combination of EDTA and an appropriate buffer effectively minimizes both pH dependent frequency shifts and ionic strength dependent intensity variations in urine NMR spectra. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

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.

     

ASICS: an automatic method for identification and quantification of metabolites in complex 1D <Superscript>1</Superscript>H NMR spectra

Introduction

Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.

Objectives

In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.

Methods

A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues.

Results

The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.

Conclusion

ASICS is a completely automated procedure to identify and quantify metabolites in ¹H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.

     

Application of BATMAN and BAYESIL for quantitative <Superscript>1</Superscript>H-NMR based metabolomics of urine: discriminant analysis of lean,obese, and obese-diabetic rats

Introduction

BATMAN and BAYESIL are software tools, which can provide a solution for automated metabolite quantifications based on the proton nuclear magnetic resonance (¹H-NMR) spectral data of bio-fluids. However, their specific application for the quantitative ¹H-NMR based metabolomics of urine has not been investigated.

Objectives

The aim of this study is to evaluate the performance of BATMAN and BAYESIL in the quantitative metabolite analysis of urine based on its ¹H-NMR spectra.

Methods

BATMAN and BAYESIL were used for automated metabolite quantification based on the ¹H-NMR spectra of the urine from the lean, obese and obese-diabetic rat groups. PLS-DA model was used to discriminate the three different groups based on the results from the quantifications.

Results

BATMAN was found to be superior to BAYESIL in identifying and quantifying the metabolites in the urine samples, owing to its flexibility that allows users to define and adjust the relevant signals of the pure standard metabolites in the database in order to fit the signals in the samples, a necessary step since variations and peak shift are natural in most ¹H-NMR spectra. The results of BATMAN also agreed well with that of the manual deconvolution method, which indicated the higher accuracy in metabolite quantification, despite the need of pre-processing and longer processing time than BAYESIL. However, in the case where the problems in baseline correction and peak shift of ¹H-NMR spectra are absent, the use of BAYESIL is more advantageous. Application of quantitative ¹H-NMR based metabolomics of the urine showed that PLS-DA model derived from BATMAN could satisfactorily discriminate the lean, obese, and obese-diabetic rat groups.

Conclusion

Both BATMAN and BAYESIL are useful for the quantitative automation of urine metabolites based on its ¹H-NMR spectra. The results from BATMAN method is superior to BAYESIL but require expertise in spectroscopy and longer computer time. Both methods help in simplifying the interpretation of metabolite status in the VIP analysis.

     

<Superscript>1</Superscript>H NMR based metabolomic approach to monitoring of the head and neck cancer treatment toxicity

Introduction

Anticancer treatment results in temporary or permanent toxicity considered as changes in normal tissues and/or involved regions. The net effect is mirrored in morphological, functional and molecular disturbances—thus in a systemic response of the human body. To date, specific NMR biomarkers of radiation therapy toxicity in head and neck squamous cell carcinoma (HNSCC) patients are scarce or even missing.

Objectives

We aimed to investigate molecular processes reflecting acute radiation sequelae (ARS) in HNSCC patients using NMR-based metabolomics of blood serum.

Methods

45 patients with HNSCC were treated with radiotherapy (RT) or chemoradiotherapy (CHRT). Blood samples were collected within a week after RT/CHRT completion. Patients were divided into two classes (of high and low ARS) on the basis of the highest individual ARS value observed during the treatment. ¹H NMR spectra of serum samples were acquired on a Bruker 400.13 MHz spectrometer at 310 K and analyzed using principal component analysis and orthogonal partial least squares discriminant analysis. Additional statistical analyses were performed on quantified metabolites.

Results

1D projections of the J-resolved NMR spectra seem to be of the great potential in the quest for the HNSCC treatment toxicity biomarker. The metabolic features characteristic for high ARS are the increased signals of N-acetyl-glycoprotein and acetate, as well as decrease of choline and the metabolites involved in energy metabolism: branched chain amino acids (BCAAs), alanine, creatinine and carnitine. Furthermore, we observed significant correlations between N-acetyl-glycoprotein and clinical markers of inflammation as well as acetate and a percentage-weight-loss during the treatment. CRP was also negatively correlated with alanine and BCAAs.

Conclusion

NMR-based metabolomics provides relevant biomarkers of RT/CHRT toxicity (ARS) in HNSCC patients. The results indicate at least three concomitant processes related to high ARS: inflammation, altered energy metabolism and disturbed membrane metabolism, and indicate an exciting potential of J-resolved NMR spectroscopy combined with multivariate projection techniques.

     

<Superscript>1</Superscript>H NMR-based metabolomics combined with HPLC-PDA-MS-SPE-NMR for investigation of standardized <Emphasis Type="Italic">Ginkgo biloba</Emphasis> preparations

Commercial preparations of Ginkgo biloba are very complex mixtures prepared from raw leaf extracts by a series of extraction and prepurification steps. The pharmacological activity is attributed to a number of flavonoid glycosides and unique terpene trilactones (TTLs), with largely uncharacterized pharmacological profiles on targets involved in neurological disorders. It is therefore important to complement existing targeted analytical methods for analysis of Ginkgo biloba preparations with alternative technology platforms for their comprehensive and global characterization. In this work, ¹H NMR-based metabolomics and hyphenation of high-performance liquid chromatography, photo-diode array detection, mass spectrometry, solid-phase extraction, and nuclear magnetic resonance spectroscopy (HPLC-PDA-MS-SPE-NMR) were used for investigation of 16 commercially available preparations of Ginkgo biloba. The standardized extracts originated from Denmark, Italy, Sweden, and United Kingdom, and the results show that ¹H NMR spectra allow simultaneous assessment of the content as well as identity of flavonoid glycosides and TTLs based on a very simple sample-preparation procedure consisting of extraction, evaporation and reconstitution in acetone-d ₆. Unexpected or unwanted extract constituents were also easily identified in the ¹H NMR spectra, which contrasts traditional methods that depend on UV absorption or MS ionizability and usually require availability of reference standards. Automated integration of ¹H NMR spectral segments (buckets or bins of 0.02 ppm width) provides relative distribution plots of TTLs based on their H-12 resonances. The present study shows that ¹H NMR-based metabolomics is an attractive method for non-selective and comprehensive analysis of Ginkgo extracts.     

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis.?Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.     

Modelling the acid/base <Superscript>1</Superscript>H NMR chemical shift limits of metabolites in human urine

Introduction

Despite the use of buffering agents the ¹H NMR spectra of biofluid samples in metabolic profiling investigations typically suffer from extensive peak frequency shifting between spectra. These chemical shift changes are mainly due to differences in pH and divalent metal ion concentrations between the samples. This frequency shifting results in a correspondence problem: it can be hard to register the same peak as belonging to the same molecule across multiple samples. The problem is especially acute for urine, which can have a wide range of ionic concentrations between different samples.

Objectives

To investigate the acid, base and metal ion dependent ¹H NMR chemical shift variations and limits of the main metabolites in a complex biological mixture.

Methods

Urine samples from five different individuals were collected and pooled, and pre-treated with Chelex-100 ion exchange resin. Urine samples were either treated with either HCl or NaOH, or were supplemented with various concentrations of CaCl₂, MgCl₂, NaCl or KCl, and their ¹H NMR spectra were acquired.

Results

Nonlinear fitting was used to derive acid dissociation constants and acid and base chemical shift limits for peaks from 33 identified metabolites. Peak pH titration curves for a further 65 unidentified peaks were also obtained for future reference. Furthermore, the peak variations induced by the main metal ions present in urine, Na⁺, K⁺, Ca²⁺ and Mg²⁺, were also measured.

Conclusion

These data will be a valuable resource for ¹H NMR metabolite profiling experiments and for the development of automated metabolite alignment and identification algorithms for ¹H NMR spectra.

     

Transgressive segregation of primary and secondary metabolites in F<Subscript>2</Subscript> hybrids between <Emphasis Type="Italic">Jacobaea aquatica</Emphasis> and <Emphasis Type="Italic">J. vulgaris</Emphasis>

Hybridization between plant species can have a number of biological consequences; interspecific hybridization has been tied to speciation events, biological invasions, and diversification at the level of genes, metabolites, and phenotypes. This study aims to provide evidence of transgressive segregation in the expression of primary and secondary metabolites in hybrids between Jacobaea vulgaris and J. aquaticus using an NMR-based metabolomic profiling approach. A number of F₂ hybrid genotypes exhibited metabolomic profiles that were outside the range encompassed by parental species. Expression of a number of primary and secondary metabolites, including jacaronone analogues, chlorogenic acid, sucrose, glucose, malic acid, and two amino acids was extreme in some F₂ hybrid genotypes compared to parental genotypes, and citric acid was expressed in highest concentrations in J. vulgaris. Metabolomic profiling based on NMR is a useful tool for quantifying genetically controlled differences between major primary and secondary metabolites among plant genotypes. Interspecific plant hybrids in general, and specifically hybrids between J. vulgaris and J. aquatica, will be useful for disentangling the ecological role of suites of primary and secondary metabolites in plants, because interspecific hybridization generates extreme metabolomic diversity compared to that normally observed between parental genotypes.     

Metabolomic analysis using optimized NMR and statistical methods

NMR-based metabolomics requires robust automated methodologies, and the accuracy of NMR-based metabolomics data is greatly influenced by the reproducibility of data acquisition and processing methods. Effective water resonance signal suppression and reproducible spectral phasing and baseline traces across series of related samples are crucial for statistical analysis. We assess robustness, repeatability, sensitivity, selectivity, and practicality of commonly used solvent peak suppression methods in the NMR analysis of biofluids with respect to the automated processing of the NMR spectra and the impact of pulse sequence and data processing methods on the sensitivity of pattern recognition and statistical analysis of the metabolite profiles. We introduce two modifications to the excitation sculpting pulse sequence whereby the excitation solvent suppression pulse cascade is preceded by low-power water resonance presaturation pulses during the relaxation delay. Our analysis indicates that combining water presaturation with excitation sculpting water suppression delivers the most reproducible and information-rich NMR spectra of biofluids.     

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples.     

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.

     

<Superscript>1</Superscript>H NMR-based metabolic profiling and target analysis: a combined approach for the quality control of <Emphasis Type="Italic">Thymus vulgaris</Emphasis>

The characterization of T. vulgaris plant material for quality control purposes was performed by NMR-based methods. Direct extraction of 141 T. vulgaris samples with DMSO-d ₆ enabled the obtainment of crude extracts with a representative composition in terms of both volatile and non-volatile constituents. The acquisition of 600 MHz ¹H NMR spectra resulted in a dataset which was analyzed by a combination of metabolic profiling and target analysis approaches. Preliminary analysis of the ¹H NMR spectra was performed by principal component analysis, which revealed sample discrimination on a chemotype basis (thymol, carvacrol and linalool chemotypes). Further minor discriminative constituents were identified as p-cymene, γ-terpinene, rosmarinic acid, and 3,4,3′,4′-tetrahydroxy-5,5′-diisopropyl-2,2′-dimethylbiphenyl. Metabolite identification was accomplished by 1D and 2D NMR techniques and supported by spiking experiments. Fast dereplication of constituents not available as reference compounds was performed by HPLC–SPE–NMR experiments. A targeted approach based on qHNMR was validated for quantification of the identified secondary metabolites. Validation was performed in terms of precision (intra-day RSD ≤ 4.51%, inter-day RSD ≤ 4.18%), repeatability (RSD ≤ 2.30%), accuracy (recovery rates within 93.4 and 103.4%), linearity (correlation coefficients ≥ 0.9990), robustness, and stability. The amount of the dominant monoterpene in thymol, carvacrol, and linalool chemotypes was respectively found to be within 0.4–2.6, 0.7–2.3, and 1.1–3.6% (w/w). Variable amounts of the precursors p-cymene and γ-terpinene were found in thymol and carvacrol chemotypes. The highest amount of rosmarinic acid and 3,4,3′,4′-tetrahydroxy-5,5′-diisopropyl-2,2′-dimethylbiphenyl in the analyzed samples was respectively 4.6 and 0.4% (w/w). Since quantification is performed on a weight basis, the essential oil content can be estimated based on the sum of the quantified monoterpenes. The NMR-based analysis of T. vulgaris represents a more comprehensive approach in comparison to traditional chromatographic methods such as GC and LC, respectively employed for the analysis of volatile and non-volatile constituents. Further advantages lie in the simple sample preparation, rapidity and reproducibility of the NMR analysis.     

Metabolic profiling by <Superscript>1</Superscript>H NMR spectroscopy of saliva shows clear distinction between control and diseased case of periodontitis

Introduction

Periodontitis is a chronic, non-reversible inflammatory disease of the oral cavity leading to destruction of periodontal tissues. Thus, the estimation of bacterial metabolite, tissue damage and secretory metabolites of the triggered inflammatory cells likely to yield results. It may be of value for understanding the pathophysiology of the disease by metabolic profiling of saliva samples using high-resolution NMR spectroscopy.

Objective

The study will evaluate the difference in salivary metabolites in healthy and periodontal condition along with fetching of possible biomarkers in case of chronic periodontitis.

Methods

¹H- NMR spectroscopy has been employed in 114 saliva samples in search of distinctive differences and spectral data were further subjected to multivariate analysis.

Result

One-hundred metabolites were characterised and assigned in the ¹H NMR spectra of saliva. The statistical analysis of control (Healthy subjects) and diseased (Periodontal subjects) using PLS-DA model resulted in R² of 0.84 and Q² of 0.79. There was an elevation in the concentration of statistically discriminant metabolites. The twenty newly identified metabolites in saliva indicates bacterial population shift along with change in homeostasis. These disturbs the biofilm, a real protector against any possible bio-damage on tooth surface. These newly identified metabolites could define better geographically diversified periodontal condition.

Conclusion

Analysis clearly differentiates healthy subjects from the diseased ones. Few newly identified metabolites along with the pool of metabolites may serve as biomarkers for distinguishing the severity and complexity of periodontitis.

     

NMR-based plant metabolomics: where do we stand, where do we go?

NMR-based metabolomics is an important tool for studying biological systems and has been applied in various organisms, including animals, plants and microbes. NMR is able to provide a 'holistic view' of the metabolites under certain conditions, and thus is advantageous for metabolomic studies. To maximize the use of the information obtained, it is also important to create a platform to measure, store and share data. Public databases for storing and sharing information are still lacking for NMR-based metabolomic analysis in plants. Such databases are urgently needed to make metabolic profiling a real omics technology. In addition, to understand metabolic processes in depth, single-cell analysis and the turnover of metabolites in pathways (fluxomics) should be measured.     

Automated SPE-RP-HPLC fractionation of biofluids combined to off-line NMR spectroscopy for biomarker identification in metabonomics

NMR-based metabonomics is a valuable and straightforward approach to measuring hundreds of metabolites in complex biofluids. However, metabolite identification is sometimes limited by overlapped signals in NMR spectra. We describe a new methodology using an automated hyphenation of solid phase extraction (SPE) with RP-HPLC combined to NMR spectroscopy, which allowed identification of 72 metabolites of various molecular classes in human urine. This methodology was also successfully applied to the fractionation of a cat urine sample to aid identification of aromatic compounds and felinine. The SPE-RP-HPLC method appears to be a reliable tool to support biomarker discovery in metabonomic studies.     

Comparison of 1D and 2D NMR spectroscopy for metabolic profiling

High-resolution, liquid state nuclear magnetic resonance (NMR) spectroscopy is a popular platform for metabolic profiling because the technique is nondestructive, quantitative, reproducible, and the spectra contain a wealth of biochemical information. Because of the large dynamic range of metabolite concentrations in biofluids, statistical analyses of one-dimensional (1D) proton NMR data tend to be biased toward selecting changes in more abundant metabolites. Although two-dimensional (2D) proton-proton experiments can alleviate spectral crowding, they have been mainly used for structural determination. In this study, 2D total correlation spectroscopy NMR was used to compare the global metabolic profiles of urine obtained from wild-type and Abcc6-knockout mice. The 2D data were compared to an improved 1D experiment in which signal contributions from macromolecules and the urea peak have been spectroscopically removed for more accurate quantitation of low-abundance metabolites. Although statistical models from both 1D and 2D data could differentiate samples acquired from the two groups of mice, only the 2D spectra allowed the characterization of statistically relevant changes in the low-abundance metabolites. While acquisition of the 2D data require more time, the data obtained resulted in a more meaningful and comprehensive metabolic profile, aided in metabolite identifications, and minimized ambiguities in peak assignments.     

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples.

     

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.