Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of urine

In this study, 1H NMR-based metabonomics has been applied, for the first time to our knowledge, to investigate lung cancer metabolic signatures in urine, aiming at assessing the diagnostic potential of this approach and gaining novel insights into lung cancer metabolism and systemic effects. Urine samples from lung cancer patients (n = 71) and a control healthy group (n = 54) were analyzed by high resolution 1H NMR (500 MHz), and their spectral profiles subjected to multivariate statistics, namely, Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Projections to Latent Structures (OPLS)-DA. Very good discrimination between cancer and control groups was achieved by multivariate modeling of urinary profiles. By Monte Carlo Cross Validation, the classification model showed 93% sensitivity, 94% specificity and an overall classification rate of 93.5%. The possible confounding influence of other factors, namely, gender and age, have also been modeled and found to have much lower predictive power than the presence of the disease. Moreover, smoking habits were found not to have a dominating influence over class discrimination. The main metabolites contributing to this discrimination, as highlighted by multivariate analysis and confirmed by spectral integration, were hippurate and trigonelline (reduced in patients), and β-hydroxyisovalerate, α-hydroxyisobutyrate, N-acetylglutamine, and creatinine (elevated in patients relatively to controls). These results show the valuable potential of NMR-based metabonomics for finding putative biomarkers of lung cancer in urine, collected in a minimally invasive way, which may have important diagnostic impact, provided that these metabolites are found to be specifically disease-related.     

Can Urine Metabolomics Be Helpful in Differentiating Neuropathic and Nociceptive Pain? A Proof-of-Concept Study

The diagnosis of pain nature is a troublesome task and a wrong attribution often leads to an increase of costs and to avoidable pharmaceutical adverse reactions. An objective and specific approach to achieve this diagnosis is highly desirable. The aim of this work was to investigate urine samples collected from patients suffering from pain of different nature by a metabolomics approach based on ¹H NMR spectroscopy and multivariate statistical analysis. We performed a prospective study on 74 subjects: 37 suffering from pain (12 with nociceptive and 25 with neuropathic pain), and 37 controls not suffering from any kind of chronic pain. The application of discriminant analysis on the urine spectral profiles allowed us to classify these two types of pain with high sensibility and specificity. Although the classification relies on the global urine metabolic profile, the individual contribution in discriminating neuropathic pain patients of metabolites such as choline and phosphocholine, taurine and alanine, suggests potential lesions to the nervous system. To the best of our knowledge, this is the first time that a urine metabolomics profile is used to classify these two kinds of pain. This methodology, although based on a limited sample, may constitute the basis for a new helpful tool in the clinical diagnosis.     

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.     

A new meta-ANOVA approach for synthesizing information under signal-heterogeneity setting with application to nuclear magnetic resonance spectroscopic data

A new synthesizing statistical methodology is proposed to resolve issues of signal-heterogeneity in data sets collected through high-resolution ¹H nuclear magnetic resonance (NMR) spectroscopy. This signal-heterogeneity is typically caused by subjective operations for processing spectral profiles and measuring peak areas, non-homogeneous biological phases of experimental subjects, and variations of systems in multi-center. All these causes are likely to simultaneously impact signals of metabolic changes and their precision in a nonlinear fashion. As a combined effect, signal-heterogeneity chiefly manifests through non-homomorphic patterns of standardized treatment mean deviations spanning all experiments, and makes most remedial statistical models with linearity structure invalid. By avoiding a huge and very complex model, we develop a simple meta-ANOVA approach to synthesize many one-way-layout ANOVA analyses from individual experiments. A scale-invariant F-ratio statistic is taken as the summarizing sufficient statistic of a non-centrality parameter that supposedly captures the information about metabolic change from each experiment. Then a joint-likelihood function of a common non-centrality is constructed as the basis for maximum likelihood estimation and Chi-square likelihood ratio testing for statistical inference. We apply the meta-ANOVA to detect metabolic changes of three metabolites identified through pattern recognition on NMR spectral profiles obtained from muscle and liver tissues. We also detect effect differences among different treatments via meta-ANOVA multiple comparison.     

Revealing the metabonomic variation of EC using 1H-NMR spectroscopy and its association with the clinicopathological characteristics

In this study, (1)H NMR-based metabonomics has been applied to investigate esophageal cancer metabolic signatures in plasma and urine, purpose of assessing the diagnostic potential of this approach and gaining novel insights into esophageal cancer metabolism and systemic effects. Plasma and urine samples from esophageal cancer patients (n = 108) and a control healthy group (n = 40) were analyzed by Nuclear Magnetic Resonance (NMR) spectroscopy (600 MHz), and their spectral profiles subjected to Orthogonal Projections to Latent Structures (OPLS-DA) for multivariate statistics. Potential metabolic biomarkers were identified using data base comparisons used for examining the significance of metabolites. Compared to healthy controls, esophageal cancer plasma had higher levels of dimethylamine, α-glucose, β-glucose, citric acid, together with lower levels of Leucine, alanine, isoleucine, valine, glycoprotein, lactate, acetone, acetate, choline, isobutyrate, unsaturated lipid, VLDL, LDL, 1-methylhistidine; Compared to healthy controls, esophageal cancer urine had higher levels of Mannitol, glutamate, γ-propalanine, phenylalanine, acetate, allantoin, pyruvate, tyrosine, β-glucose and guinolinate, together with lower levels of N-acetylcysteine, valine, dihydrothymine, hippurate, methylguanidine, 1-methylnicotin- amide and Citric acid; Very good discrimination between cancer and control groups was achieved by multivariate modeling of plasma and urinary profiles. (1)H NMR-based metabolite profiling analysis was shown to be an effective approach to differentiating between patients with EC and healthy subjects. Good sensitivity and selectivity were shown by using the metabolite markers discovered to predict the classification of samples from the healthy control group and the patients with the disease. Plasma and urine metabolic profiling may have potential for early diagnosis of EC and may enhance our understanding of its mechanisms.     

NMR spectroscopy based metabonomic studies on the comparative biochemistry of the kidney and urine of the bank vole (Clethrionomys glareolus), wood mouse (Apodemus sylvaticus), white toothed shrew (Crocidura suaveolens) and the laboratory rat

The metabolic profiles of three wild mammals that vary in their trophic strategies, the herbivorous bank vole (Clethrionomys glareolus), the granivorous wood mouse (Apodemus sylvaticus), and the insectivorous white-toothed shrew (Crocidura suaveolens), were compared with that of a widely used strain of laboratory rat (Sprague Dawley). In conjunction with NMR spectroscopic investigations into the urine and blood plasma composition for these mammals, high resolution magic angle spinning (HRMAS) 1H-nuclear magnetic resonance (NMR) spectroscopy was applied to investigate the composition of intact kidney samples. Adaptation to natural diet affects both renal metabolism and urinary profiles, and while these techniques have been used to study the metabolism of the laboratory rat little is known about wild small mammals. The species were readily separated by their urinary profiles using either crude metabolite ratios or statistical pattern recognition. Bank vole urine contained higher concentrations of aromatic amino acids compared with the other small mammals, while the laboratory rats produced relatively more hippurate. HRMAS 1H-NMR demonstrated striking differences in both lipid concentration and composition between the wild mammals and Sprague Dawley rats. Bank voles contained high concentrations of the aromatic amino acids phenylalanine, tyrosine and tryptophan in all tissue and biofluids studied. This study demonstrates the analytical power of combined NMR techniques for the study of inter-species metabolism and further demonstrates that metabolic data acquired on laboratory animals cannot be extended to wild species.     

Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts

Metabolic profiling, metabolomic and metabonomic studies mainly involve the multicomponent analysis of biological fluids, tissue and cell extracts using NMR spectroscopy and/or mass spectrometry (MS). We summarize the main NMR spectroscopic applications in modern metabolic research, and provide detailed protocols for biofluid (urine, serum/plasma) and tissue sample collection and preparation, including the extraction of polar and lipophilic metabolites from tissues. 1H NMR spectroscopic techniques such as standard 1D spectroscopy, relaxation-edited, diffusion-edited and 2D J-resolved pulse sequences are widely used at the analysis stage to monitor different groups of metabolites and are described here. They are often followed by more detailed statistical analysis or additional 2D NMR analysis for biomarker discovery. The standard acquisition time per sample is 4-5 min for a simple 1D spectrum, and both preparation and analysis can be automated to allow application to high-throughput screening for clinical diagnostic and toxicological studies, as well as molecular phenotyping and functional genomics.     

Discrimination of Basal Cell Carcinoma from Normal Skin Tissue Using High-Resolution Magic Angle Spinning 1H NMR Spectroscopy

High-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy is a useful tool for investigating the metabolism of various cancers. Basal cell carcinoma (BCC) is the most common skin cancer. However, to our knowledge, data on metabolic profiling of BCC have not been reported in the literature. The objective of the present study was to investigate the metabolic profiling of cutaneous BCC using HR-MAS ¹H NMR spectroscopy. HR-MAS ¹H NMR spectroscopy was used to analyze the metabolite profile and metabolite intensity of histopathologically confirmed BCC tissues and normal skin tissue (NST) samples. The metabolic intensity normalized to the total spectral intensities in BCC and NST was compared, and multivariate analysis was performed with orthogonal partial least-squares discriminant analysis (OPLS-DA). P values < 0.05 were considered statistically significant. Univariate analysis revealed 9 metabolites that showed statistically significant difference between BCC and NST. In multivariate analysis, the OPLS-DA models built with the HR-MAS NMR metabolic profiles revealed a clear separation of BCC from NST. The receiver operating characteristic curve generated from the results revealed an excellent discrimination of BCC from NST with an area under the curve (AUC) value of 0.961. The present study demonstrated that the metabolite profile and metabolite intensity differ between BCC and NST, and that HR-MAS ¹H NMR spectroscopy can be a valuable tool in the diagnosis of BCC.     

Selection of biomarkers by a multivariate statistical processing of composite metabonomic data sets using multiple factor analysis

We introduce a statistical approach for integrating data from several analytical platforms. We illustrate this approach using (1)H-(13)C Heteronuclear Multiple Bond Connectivity nuclear magnetic resonance spectroscopy ((1)H-(13)C HMBC NMR) and Pyrolysis Metastable Atom Bombardment Time-of-Flight mass spectrometry (Py-MAB-TOF-MS) to perform metabolic fingerprinting on cattle treated with anabolic steroids. Multiple factor analysis (MFA) integrates complementary aspects from NMR and MS data into a unique metabolic signature describing the biomarkers related to the dose-response. This work also indicates that, from a practical point of view, metabonomics and other "-omics" biotechnologies can benefit significantly from a generalized multi-platform integrative approach using multiple factor analysis.     

Homeostatic signature of anabolic steroids in cattle using 1H-13C HMBC NMR metabonomics

We used metabonomics to discriminate the urinary signature of different anabolic steroid treatments in cattle having different physiological backgrounds (age, sex, and race). (1)H-(13)C heteronuclear multiple bonding connectivity NMR spectroscopy and multivariate statistical methods reveal that metabolites such as trimethylamine-N-oxide, dimethylamine, hippurate, creatine, creatinine, and citrate characterize the biological fingerprint of anabolic treatment. These urinary biomarkers suggest an overall homeostatic adaptation in nitrogen and energy metabolism. From results obtained in this study, it is now possible to consider metabonomics as a complementary method usable to improve doping control strategies to detect fraudulent anabolic treatment in cattle since the oriented global metabolic response provides helpful discrimination.     

Taxonomic discrimination of cyanobacteria by metabolic fingerprinting using proton nuclear magnetic resonance spectra and multivariate statistical analysis

When whole-cell extracts are analyzed, proton nuclear magnetic resonance (¹H NMR) spectroscopy provides biochemical profiles that contain overlapping signals of the majority of the compounds. To determine whether cyanobacteria could be taxonomically discriminated on the basis of metabolic fingerprinting, we subjected whole-cell extracts of the cyanobacteria to¹H NMR. The¹H NMR spectra revealed a predominance of signals in the aliphatic region. Principal component analysis (PCA) of the data then enabled discrimination of the cyanobacteria. The hierarchical dendrogram, based on PCA of the aliphatic region data, showed that six cyanobacterial taxa were discriminated from two eukaryotic microalgal species, and that the six taxa could be subsequently divided into three groups. This agrees with the current taxonomy of cyanobacteria. Therefore, our overall results indicate that metabolic fingerprinting using¹H NMR spectra and multivariate statistical analysis provide a simple, rapid method for the taxonomical discrimination of cyanobacteria.     

Gas chromatography/mass spectrometry in metabolic profiling of biological fluids

One of the objectives of metabonomics is to identify subtle changes in metabolite profiles between biological systems of different physiological or pathological states. Gas chromatography mass spectrometry (GC/MS) is a widely used analytical tool for metabolic profiling in various biofluids, such as urine and blood due to its high sensitivity, peak resolution and reproducibility. The availability of the GC/MS electron impact (EI) spectral library further facilitates the identification of diagnostic biomarkers and aids the subsequent mechanistic elucidation of the biological or pathological variations. With the advent of new comprehensive two dimensional GC (GCxGC) coupled to time-of-flight mass spectrometry (TOFMS), it is possible to detect more than 1200 compounds in a single analytical run. In this review, we discuss the applications of GC/MS in the metabolic profiling of urine and blood, and discuss its advances in methodologies and technologies.     

Investigating pre‐analytical requirements for serum and plasma based infrared spectro‐diagnostic

Infrared spectroscopy is a rapid, easy‐to‐operate, label‐free and therefore cost‐effective technique. Many studies performed on biofluids (eg, serum, plasma, urine, sputum, bile and cerebrospinal fluid) have demonstrated its promising application as a clinical diagnostic tool. Given all these characteristics, infrared spectroscopy appears to be an ideal candidate to be implemented into the clinics. However, before considering its translation, a clear effort is needed to standardise protocols for biofluid spectroscopic analysis. To reach this goal, careful investigations to identify and track errors that can occur during the pre‐analytical phase is a crucial step. Here, we report for the first time, results of investigations into pre‐analytical factors that can affect the quality of the spectral data acquired on serum and plasma, such as the impact of long‐term freezing time storage of samples as well as the month‐to‐month reproducibility of the spectroscopic analysis. The spectral data discrimination has revealed to be majorly impacted by a residual water content variation in serum and plasma dried samples.      

Cross-platform analysis of longitudinal data in metabolomics

Metabolic profiling is considered to be a very promising tool for diagnostic purposes, for assessing nutritional status and response to drugs. However, it is also evident that human metabolic profiles have a complex nature, influenced by many external factors. This, together with the understanding of the difficulty to assign people to distinct groups and a general move in clinical science towards personalized medicine, raises the interest to explore individual and variable metabolic features for each individual separately in longitudinal study design. In the current paper we have analyzed a set of metabolic profiles of a selection of six urine samples per person from a set of healthy individuals by (1)H NMR and reversed-phase UPLC-MS. We have demonstrated that the method for recovery of individual metabolic phenotypes can give complementary information to another established method for analysis of longitudinal data--multilevel component analysis. We also show that individual metabolic signatures can be found not only in (1)H NMR data, as has been demonstrated before, but also even more strongly in LC-MS data.     

NMR spectroscopy based metabolic profiling of urine and serum for investigation of physiological perturbations during radiation sickness

Radiation accidents are rare events that induce radiation syndrome, a complex pathology which is difficult to treat. In medical management of radiation victims, life threatening damage to different physiological systems should be taken into consideration. The present study was proposed to identify metabolic and physiological perturbations in biofluids of mice during different phases of radiation sickness using ¹H nuclear magnetic resonance (¹H NMR) spectroscopy and pattern recognition (PR) technique. The ¹H NMR spectra of the biofluids collected from mice irradiated with 5 Gray (Gy) at different time points during radiation sickness were analysed visually and by principal components analysis. Urine and serum spectral profile clearly showed altered metabolic profiles during different phases of radiation sickness. Increased concentration of urine metabolites viz. citrate, α ketoglutarate, succinate, hippurate, and trimethylamine during prodromal and clinical manifestation phase of radiation sickness shows altered gut microflora and energy metabolism. On the other hand, serum nuclear magnetic resonance (NMR) spectra reflected changes associated with lipid, energy and membrane metabolism during radiation sickness. The metabonomic time trajectory based on PR analysis of ¹H NMR spectra of urine illustrates clear separation of irradiated mice group at different time points from pre dose. The difference in NMR spectral profiles depicts the pathophysiological changes and metabolic disturbances observed during different phases of radiation sickness, that in turn, demonstrate involvement of multiple organ dysfunction. This could further be useful in development of multiparametric approach for better evaluation of radiation damage as well as for medical management during radiation sickness.     

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.     

1H-NMR-based metabolomics for cancer targeting and metabolic engineering –A review

Nuclear magnetic resonance (NMR) spectroscopy acts as the best tool that can be used in tissue engineering scaffolds to investigate unknown metabolites. Moreover, metabolomics is a systems approach for examining in vivo and in vitro metabolic profiles, which promises to provide data on cancer metabolic alterations. However, metabolomic profiling allows for the activity of small molecules and metabolic alterations to be measured. Furthermore, metabolic profiling also provides high-spectral resolution, which can then be linked to potential metabolic relationships. An altered metabolism is a hallmark of cancer that can control many malignant properties to drive tumorigenesis. Metabolite targeting and metabolic engineering contribute to carcinogenesis by proliferation, and metabolic differentiation. The resulting the metabolic differences are examined with traditional chemometric methods such as principal component analysis (PCA), and partial least squares-discriminate analysis (PLS-DA). In this review, we examine NMR-based activity metabolomic platforms that can be used to analyze various fluxomics and for multivariant statistical analysis in cancer. We also aim to provide the reader with a basic understanding of NMR spectroscopy, cancer metabolomics, target profiling, chemometrics, and multifunctional tools for metabolomics discrimination, with a focus on metabolic phenotypic diversity for cancer therapeutics.     

Investigation of a wide spectrum of inherited metabolic disorders by 13C NMR spectroscopy

High-resolution (1)H NMR spectroscopy of body fluids has proved to be very useful in diagnostics of inherited metabolic diseases, whereas (13)C NMR remains almost unexploited. In this paper the application of (13)C NMR spectroscopy of fivefold concentrated urine samples for diagnosis of selected metabolic diseases is reported. Various marker metabolites were identified in test urine samples from 33 patients suffering from 10 different diseases, providing information which could be crucial for their diagnoses. Spectra were accumulated for 2 h or overnight when using spectrometers operating at 9.4 or 4.7 T magnetic fields, respectively. Interpretation of the measurement results was based on a comparison of the peak positions in the measured spectrum with reference data. The paper contains a table with (13)C NMR chemical shifts of 73 standard compounds. The method can be applied individually or as an auxiliary technique to (1)H NMR or any other analytical method.     

Urine stability for metabolomic studies: effects of preparation and storage

Metabolomic studies attempt to identify and profile unique metabolic differences among test populations, which may be correlated with a specific biological stress or pathophysiology. Due to the ease of collection and the metabolite-rich nature of urine, it is frequently used as a bio-fluid for human and animal metabolic studies. High-resolution ¹H-NMR is an analytical tool used to qualitatively and quantitatively identify metabolites in urine. Urine samples were collected from healthy male and female subjects and prepared: raw, following centrifugation, filtration, or the addition of the bacteriostatic preservative sodium azide and analyzed by NMR. In addition, these samples were stored at room temperature (22 °C), in a refrigerator (4 °C), or in a deep-freeze (−80 °C). Samples were analyzed by NMR every week for a month and changes in concentrations of 55 easily identifiable metabolites were followed. The degree of change in metabolite concentrations following storage over a 4-week period were influenced by the different methods of sample preparation and storage. Significant changes in urine metabolites are likely due to bacterial contamination of the urine. Our study demonstrates that bacterial contamination of urine in normal individuals significantly alters the metabolic profile of urine over time and proper preparation and storage procedures must be followed to reduce these changes. By identifying appropriate methods of urine preparation and storage investigators will preserve the fidelity of the urine samples in order to better reflect the original metabolic state.     

Investigations into biochemical changes due to diurnal variation and estrus cycle in female rats using high-resolution (1)H NMR spectroscopy of urine and pattern recognition

Metabonomic methods utilizing (1)H NMR spectroscopy and pattern recognition analysis (NMR-PR) have been applied to investigate biochemical variation in a control population of female rats over time in relation to diurnal and estrus cycle fluctuations. Urine samples were collected twice daily (6 AM-6 PM and 6 PM-6 AM) from female rats (n = 10) for a period of 10 days. (1)H NMR spectroscopic analysis and PR were performed on each sample. Subtle differences in the endogenous metabolite excretion profiles of urine samples at the various stages of the estrus cycle were observed. The main inherent metabolic clustering in the principal components analysis (PCA) maps was related to interrat variation and was observed in the first two principal components (PCs), accounting for 66% of the variance in these data. Separation of urinary data according to time of sampling (day and night) was achieved in the lower PCs. Some of the differences in the urinary profiles of day and night samples causing this separation were attributed to the increase in metabolic activity of the rat during the night. Individual rat data were also mapped as a function of time, using PCA, to produce a metabolic trajectory, which in a number of cases facilitated separation of one or more stages of the estrus cycle. Several of the fluctuations observed between urine samples collected during the different stages of the estrus cycle may be related to hormone levels. Although variation in metabolite profiles relating to both diurnal and hormonal variation could be detected these perturbations were minor compared with the effects observed due to interrat variation. This is the first time that a hormonal cycle has been described for individuals based on NMR spectroscopic and multivariate analysis of metabolic data and shows the value of metabonomic methods in the investigation of physiological variation and rhythms.