Improved analysis of multivariate data by variable stability scaling: application to NMR-based metabolic profiling

The Impact of Delayed Storage on the Measured Proteome and Metabolome of Human Cerebrospinal Fluid

BACKGROUND

Because cerebrospinal fluid (CSF) is in close contact with diseased areas in neurological disorders, it is an important source of material in the search for molecular biomarkers. However, sample handling for CSF collected from patients in a clinical setting might not always be adequate for use in proteomics and metabolomics studies.

METHODS

We left CSF for 0, 30, and 120 min at room temperature immediately after sample collection and centrifugation/removal of cells. At 2 laboratories CSF proteomes were subjected to tryptic digestion and analyzed by use of nano-liquid chromatography (LC) Orbitrap mass spectrometry (MS) and chipLC quadrupole TOF-MS. Metabolome analysis was performed at 3 laboratories by NMR, GC-MS, and LC-MS. Targeted analyses of cystatin C and albumin were performed by LC–tandem MS in the selected reaction monitoring mode.

RESULTS

We did not find significant changes in the measured proteome and metabolome of CSF stored at room temperature after centrifugation, except for 2 peptides and 1 metabolite, 2,3,4-trihydroxybutanoic (threonic) acid, of 5780 identified peptides and 93 identified metabolites. A sensitive protein stability marker, cystatin C, was not affected.

CONCLUSIONS

The measured proteome and metabolome of centrifuged human CSF is stable at room temperature for up to 2 hours. We cannot exclude, however, that changes undetectable with our current methodology, such as denaturation or proteolysis, might occur because of sample handling conditions. The stability we observed gives laboratory personnel at the collection site sufficient time to aliquot samples before freezing and storage at −80 °C.

NMR spectrometers as "magnetic tongues": prediction of sensory descriptors in canned tomatoes

The perception of odor and flavor of food is a complicated physiological and psychological process that cannot be explained by simple models. Quantitative descriptive analysis is a technique used to describe sensory features. Nevertheless, the availability of a number of instrumental techniques has opened up the possibility to calibrate the sensory perception. In this frame, we have tested the potentiality of nuclear magnetic resonance spectroscopy as a predictive tool to measure sensory descriptors. In particular, we have used an NMR metabolomic approach that allowed us to differentiate the analyzed samples based on their chemical composition. We were able to correlate the NMR metabolomic fingerprints recorded for canned tomato samples to the sensory descriptors bitterness, sweetness, sourness, saltiness, tomato and metal taste, redness, and density, suggesting that NMR might be a very useful tool for the characterization of sensory features of tomatoes.

NMR-based metabolomic investigations on the differential responses in adductor muscles from two pedigrees of Manila clam Ruditapes philippinarum to Cadmium and Zinc

Manila clam Ruditapes philippinarum is one of the most important economic species in shellfishery in China due to its wide geographic distribution and high tolerance to environmental changes (e.g., salinity, temperature). In addition, Manila clam is a good biomonitor/bioindicator in "Mussel Watch Programs" and marine environmental toxicology. However, there are several pedigrees of R. philippinarum distributed in the marine environment in China. No attention has been paid to the biological differences between various pedigrees of Manila clams, which may introduce undesirable biological variation in toxicology studies. In this study, we applied NMR-based metabolomics to detect the biological differences in two main pedigrees (White and Zebra) of R. philippinarum and their differential responses to heavy metal exposures (Cadmium and Zinc) using adductor muscle as a target tissue to define one sensitive pedigree of R. philippinarum as biomonitor for heavy metals. Our results indicated that there were significant metabolic differences in adductor muscle tissues between White and Zebra clams, including higher levels of alanine, glutamine, hypotaurine, phosphocholine and homarine in White clam muscles and higher levels of branched chain amino acids (valine, leucine and isoleucine), succinate and 4-aminobutyrate in Zebra clam muscles, respectively. Differential metabolic responses to heavy metals between White and Zebra clams were also found. Overall, we concluded that White pedigree of clam could be a preferable bioindicator/biomonitor in marine toxicology studies and for marine heavy metals based on the relatively high sensitivity to heavy metals.

Benzo(a)pyrene-induced metabolic responses in Manila clam Ruditapes philippinarum by proton nuclear magnetic resonance ((1)H NMR) based metabolomics

Benzo(a)pyrene is an important polycyclic aromatic hydrocarbon (PAH) which causes carcinogenic, teratogenic and mutagenic effects in various species and the level of contamination of this toxic agent in the marine environment is of great concern. In this study, metabolic responses induced by two doses (0.02 and 0.2μM) of BaP were characterized in the gill tissues of Manila clam Ruditapes philippinarum after exposure for 24, 48 and 96h. The high dose (0.2μM) of BaP induced the disturbances in energy metabolism and osmotic regulation based on the metabolic biomarkers such as succinate, alanine, glucose, glycogen, branched chain amino acids, betaine, taurine, homarine, and dimethylamine in clam gills after 24h of exposure. In addition, hormesis induced by BaP was found in clams exposed to both doses of BaP. Overall, our results demonstrated the applicability of metabolomics for the elucidation of toxicological effects of marine environmental contaminants in a selected bioindicator species such as the Manila clam.

Metabolic responses in gills of Manila clam Ruditapes philippinarum exposed to copper using NMR-based metabolomics

Copper is an important heavy metal contaminant with high ecological risk in the Bohai Sea. In this study, the metabolic responses in the bioindicator, Manila clam (Ruditapes philippinarum), to the environmentally relevant copper exposures were characterized using NMR-based metabolomics. The significant metabolic changes corresponding to copper exposures were related to osmolytes, intermediates of the Krebs cycle and amino acids, such as the increase in homarine, branched chain amino acids and decrease in succinate, alanine and dimethylamine in the copper-exposed clam gills during 96 h exposure period. Overall, Cu may lead to the disturbances in osmotic regulation and energy metabolism in clams during 96 h experimental period. These results demonstrate that NMR-based metabolomics is applicable for the discovery of metabolic biomarkers which could be used to elucidate the toxicological mechanisms of marine heavy metal contaminants.

Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis

Background

Analysis of Cerebrospinal Fluid (CSF) samples holds great promise to diagnose neurological pathologies and gain insight into the molecular background of these pathologies. Proteomics and metabolomics methods provide invaluable information on the biomolecular content of CSF and thereby on the possible status of the central nervous system, including neurological pathologies. The combined information provides a more complete description of CSF content. Extracting the full combined information requires a combined analysis of different datasets i.e. fusion of the data.

Results

A novel fusion method is presented and applied to proteomics and metabolomics data from a pre-clinical model of multiple sclerosis: an Experimental Autoimmune Encephalomyelitis (EAE) model in rats. The method follows a mid-level fusion architecture. The relevant information is extracted per platform using extended canonical variates analysis. The results are subsequently merged in order to be analyzed jointly. We find that the combined proteome and metabolome data allow for the efficient and reliable discrimination between healthy, peripherally inflamed rats, and rats at the onset of the EAE. The predicted accuracy reaches 89% on a test set. The important variables (metabolites and proteins) in this model are known to be linked to EAE and/or multiple sclerosis.

Conclusions

Fusion of proteomics and metabolomics data is possible. The main issues of high-dimensionality and missing values are overcome. The outcome leads to higher accuracy in prediction and more exhaustive description of the disease profile. The biological interpretation of the involved variables validates our fusion approach.

Metabonomics study of transgenic Bacillus thuringiensis rice (T2A-1) meal in a 90-day dietary toxicity study in rats

Rice is one of the most important staple foods in the world. The Cry2A gene was inserted into the rice genome to help the plant combat insects. As the unintended effects of the genetically modified (GM) organisms are the most important barriers to the promotion of GM organisms, we have carried out a useful exploration to establish a new in vivo evaluation model for genetically modified foods by metabonomics methods. In this study, the rats were fed for 90 days with the GM and NON-GM rice diets. The changes in metabolites of the urine were detected using (1)H-NMR. The metabonomics were analyzed to see whether the GM rice can induce the metabolite changes in the rats' urine when compared with the NON-GM rice group. The multivariate analysis and ANOVA were used to determine the differences and the significance of differences respectively, and eventually we concluded that these differences did not have a biological significance. The conclusion of the metabonomics was comparable with that from the traditional method. As a non-invasive and dynamic monitoring method, metabonomics will be a new way of assessing the food safety of GM foods.

Ameliorating effects of Mango (Mangifera indica L.) fruit on plasma ethanol level in a mouse model assessed with H-NMR based metabolic profiling

The ameliorating effects of Mango (Mangifera indica L.) flesh and peel samples on plasma ethanol level were investigated using a mouse model. Mango fruit samples remarkably decreased mouse plasma ethanol levels and increased the activities of alcohol dehydrogenase and acetaldehyde dehydrogenase. The ¹H-NMR-based metabolomic technique was employed to investigate the differences in metabolic profiles of mango fruits, and mouse plasma samples fed with mango fruit samples. The partial least squares-discriminate analysis of ¹H-NMR spectral data of mouse plasma demonstrated that there were clear separations among plasma samples from mice fed with buffer, mango flesh and peel. A loading plot demonstrated that metabolites from mango fruit, such as fructose and aspartate, might stimulate alcohol degradation enzymes. This study suggests that mango flesh and peel could be used as resources for functional foods intended to decrease plasma ethanol level after ethanol uptake.

Tissue-specific toxicological effects of cadmium in green mussels (Perna viridis): nuclear magnetic resonance-based metabolomics study

Toxicity tests for metals have traditionally focused on selected biomarkers to characterize the biological stress induced by metals in marine organisms. Here nuclear magnetic resonance (NMR)-based metabolomics, a system biology tool, was applied to the marine green mussel, Perna viridis, to investigate the toxicological effects of Cd in both digestive gland and adductor muscle tissues. After Cd exposure for either two or four weeks, there was no significant metabolic change in the mussels exposed to Cd at 2 µg/L. At 20 µg/L, there were major metabolite changes related to amino acids, osmolytes, and energy metabolites. Digestive gland tissue was more sensitive to Cd than adductor muscle tissue. The adductor muscle tissue showed elevated levels of glutamine, glutamate, and lactate, and reduced levels of branched chain amino acids, aspartate, phenylalanine, and tyrosine. Overall, four weeks of Cd exposure produced neurotoxicity and metabolic disturbances and disturbed osmoregulation. These results suggest that the adductor muscle tissue of mussels may be a suitable supplemental biomarker for exposure to toxicants. In addition, the results demonstrate that (1) H-NMR-based metabolomic analysis can provide a systematic view of the toxicological effects of metals on mussels, suggesting that it might be employed to investigate the toxicological effects of other marine pollutants.

Cancer biomarker discovery: Opportunities and pitfalls in analytical methods

Many diseases result in specific and characteristic changes in the chemical and biochemical profiles of biological fluids and tissues prior to development of clinical symptoms. These changes are often useful diagnostic and prognostic biomarkers. Identifying biomarkers that can be used for the early detection of cancer will result in more efficient treatments, reduction in suffering, and lower mortality rates. An ideal screening test should be non-invasive with high sensitivity and specificity. Proteomic and metabolomic analyses of biological samples can reveal changes in abundance levels of metabolites and proteins that when validated and confirmed through clinical trials can function as clinical tests for early detection, diagnosis, monitoring disease progression, and predicting therapeutic response. While the past decade has seen great advancements in proteomics and metabolomics research producing potential biomarkers for cancer, most of the identified biomarkers have failed to replace existing clinical tests. To become a clinically approved test, a potential biomarker should be confirmed and validated using hundreds of specimens and should be reproducible, specific, and sensitive. A search of the scientific and medical literature indicates that many studies report the discovery of potential biomarkers without proper validation and/or they do not meet the above criteria. In this manuscript, we will discuss the successes and the pitfalls of biomarker research and comment on study and experimental design, which in most cases is lacking, resulting in suboptimal biomarkers.

Metabolic profiling of Rhodiola rosea rhizomes by ¹H NMR spectroscopy

INTRODUCTION

Rhodiola rosea is a broadly used medicinal plant with largely unexplored natural variability in secondary metabolite levels.

OBJECTIVE

The aim of this work was to develop a non-target procedure for ¹H NMR spectroscopic fingerprinting of rhizome extracts for pattern recognition analysis and identification of secondary metabolites responsible for differences in sample composition. To achieve this, plants from three different geographic areas (Swiss Alps, Finland, and Altai region in Siberia) were investigated.

RESULTS

A sample preparation procedure was developed in order to remove polymeric polyphenols as the ¹H NMR analysis of low-molecular-weight metabolites was hampered by the presence of tannins. Principal component analysis disclosed tight clustering of samples according to population. PCA models based on the aromatic region of the spectra showed that the first two components reflected changes in the content of salidroside and rosavin, respectively, the rosavin content being negatively correlated to that of rhodiocyanoside A and minor aromatics. Score plots and non-parametric variance tests demonstrated population-dependent changes according to harvest time. Data consistency was assessed using score plots and box-and-whisker graphs. In addition, a procedure for presenting loadings of PCA models based on bucketed data as high-resolution plots, which are reminiscent of real ¹H NMR spectra and help to identify latent biomarkers, is presented.

CONCLUSION

This study demonstrated the usefulness of the established procedure for multivariate non-target ¹H NMR metabolic profiling of Rhodiola rosea.

Toxicological responses to acute mercury exposure for three species of Manila clam Ruditapes philippinarum by NMR-based metabolomics

The Manila clam (Ruditapes philippinarum) has been considered a good sentinel species for metal pollution monitoring in estuarine tidal flats. Along the Bohai coast of China, there are dominantly distributed three species of clams (White, Liangdao Red and Zebra in Yantai population) endowed with distinct tolerances to environmental stressors. In this study, adductor muscle samples were collected from both control and acute mercury exposed White, Liangdao Red and Zebra clams, and the extracts were analyzed by NMR-based metabolomics to compare the metabolic profiles and responses to the acute mercury exposure to determine the most sensitive clam species capable of acting as abioindicator for heavy metal pollution monitoring. The major abundant metabolites in the White clam sample were branched-chain amino acids (leucine, isoleucine and valine), lactate, arginine, aspartate, acetylcholine, homarine and ATP/ADP, while the metabolite profile of Zebra clam sample comprised high levels of glutamine, acetoacetate, betaine, taurine and one unidentified metabolite. For the Liangdao Red clam sample, the metabolite profile relatively exhibited high amount of branched-chain amino acids, arginine, glutamate, succinate, acetylcholine, homarine and two unassigned metabolites. After 48h exposure of 20μgL(-1) Hg(2+), the metabolic profiles showed significant differences between three clam species, which included increased lactate, succinate, taurine, acetylcholine, betaine and homarine and decreased alanine, arginine, glutamine, glutamate, acetoacetate, glycine and ATP/ADP in White clam samples, and elevated succinate, taurine and acetylcholine, and declined glutamine, glycine, and aspartate in Liangdao Red clam samples, while the increased branched-chain amino acids, lactate, succinate, acetylcholine and homarine, and reduced alanine, acetoacetate, glycine and taurine were observed in the Zebra clam samples. Overall, our findings showed that White clams could be a preferable bioindicator for the metal pollution monitoring based on the more sensitive metabolic changes in the adductor muscle compared with other two (Liangdao Red and Zebra) clam species.

Diagnostic properties of metabolic perturbations in rheumatoid arthritis

Introduction

The aim of this study was to assess the feasibility of diagnosing early rheumatoid arthritis (RA) by measuring selected metabolic biomarkers.

Methods

We compared the metabolic profile of patients with RA with that of healthy controls and patients with psoriatic arthritis (PsoA). The metabolites were measured using two different chromatography-mass spectrometry platforms, thereby giving a broad overview of serum metabolites. The metabolic profiles of patient and control groups were compared using multivariate statistical analysis. The findings were validated in a follow-up study of RA patients and healthy volunteers.

Results

RA patients were diagnosed with a sensitivity of 93% and a specificity of 70% in a validation study using detection of 52 metabolites. Patients with RA or PsoA could be distinguished with a sensitivity of 90% and a specificity of 94%. Glyceric acid, D-ribofuranose and hypoxanthine were increased in RA patients, whereas histidine, threonic acid, methionine, cholesterol, asparagine and threonine were all decreased compared with healthy controls.

Conclusions

Metabolite profiling (metabolomics) is a potentially useful technique for diagnosing RA. The predictive value was without regard to the presence of antibodies against cyclic citrullinated peptides.

NMR and MS methods for metabonomics

Metabonomics, also often referred to as "metabolomics" or "metabolic profiling," is the systematic profiling of metabolites in bio-fluids or tissues of organisms and their temporal changes. In the last decade, metabonomics has become increasingly popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabonomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabonomics, i.e., NMR, LC-MS, UPLC-MS, and GC-MS have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabonomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation, to determining the measurement details of all analytical platforms, and finally, to discussing the corresponding specific steps of data analysis.

NMR-based metabolomic studies on the toxicological effects of cadmium and copper on green mussels Perna viridis

Traditional toxicology studies have focused on selected biomarkers to characterize the biological stress induced by metals in marine organisms. In this study, a system biology tool, metabolomics, was applied to the marine mussel Perna viridis to investigate changes in the metabolic profiles of soft tissue as a response to copper (Cu) and cadmium (Cd), both as single metal and as a mixture. The major metabolite changes corresponding to metal exposure are related to amino acids, osmolytes, and energy metabolites. Following metal exposure for 1 week, there was a significant increase in the levels of branched chain amino acids, histidine, glutamate, glutamine, hypotaurine, dimethylglycine, arginine and ATP/ADP. For the Cu+Cd co-exposed mussels, the levels of lactate, branched chain amino acid, succinate, and NAD increased, whereas the levels of glucose, glycogen, and ATP/ADP decreased, indicating a different metabolic profile for the single metal exposure groups. After 2 weeks of exposure, the mussels showed acclimatization to Cd exposure based on the recovery of some metabolites. However, the metabolic profile induced by the metal mixture was very similar to that from Cu exposure, suggesting that Cu dominantly induced the metabolic disturbances. Both Cu and Cd may lead to neurotoxicity, disturbances in energy metabolism, and osmoregulation changes. These results demonstrate the high applicability and reliability of NMR-based metabolomics in interpreting the toxicological mechanisms of metals using global metabolic biomarkers.

Metabolic profiling detects field effects in nondysplastic tissue from esophageal cancer patients

The variable rate of missed cancer in endoscopic biopsies and lack of other biomarkers reduce the effectiveness of surveillance programs in esophageal cancer. Based on the "field cancerization" hypothesis that tumors arise within a transformed field with an altered biochemical phenotype, we sought to test if metabolic profiling could differentiate between histologically normal tissue from individuals with and without esophageal cancer. Thirty-five patients with esophageal adenocarcinoma and 52 age-matched controls participated in the study. Using 1H magic angle spinning-nuclear magnetic resonance spectroscopy of intact tissue, we generated metabolic profiles of tumor tissue, proximal histologically normal mucosa from cancer patients (PHINOM), and proximal histologically normal mucosa from a control group. Using multivariate regression and receiver-operator characteristic analysis, we identified a panel of metabolites discriminating malignant and histologically normal tissues from cancer patients and from that of controls. Whereas 26% and 12% of the spectral profile regions were uniquely discriminating tumor or control tissue, respectively, 5% of the profile exhibited a significant progressive change in signal intensity from controls to PHINOM to tumor. Regions identified were assigned to phosphocholine (PC), glutamate (Glu), myo-inositol, adenosine-containing compounds, uridine-containing compounds, and inosine. In particular, the PC/Glu ratio in histologically normal tissue signified the presence of esophageal cancer (n=123; area under the curve, 0.84; P<0.001). In conclusion, our findings support the hypothesis of the presence of metabonomic field effects in esophageal cancer, even in non-Barrett's segments. This indicates that metabolic profiling of tissue can potentially play a role in the surveillance of cancer by reporting on the phenotypic consequences of field cancerization.

Hsp70 expression and metabolite composition in response to short-term thermal changes in Folsomia candida (Collembola)

In the present study the joint transcriptomic and metabolomic responses in Folsomia candida (Collembola) to temperature changes on a short-term scale were studied. Change in heat tolerance was examined as survival after a 35 degrees C heat shock (2h) in the course of either a fluctuating temperature regime (8 to 32 degrees C; pre-treated) or a constant temperature (20 degrees C; control) over a period of 24h. Exposure to a temperature increase from 20 to 32 degrees C (2.4 degrees C min(-)(1)) induced a significantly increased heat tolerance which continued throughout the experiment. Expression of the gene encoding heat shock protein Hsp70 was assessed at the mRNA level using real time quantitative polymerase chain reaction (QPCR). Hsp70 was rapidly induced and significantly increased by the temperature increase. The relative concentrations of low molecular weight metabolites were analysed in F. candida using nuclear magnetic resonance spectroscopy ((1)H NMR). A significant metabolomic divergence between pre-treated and control collembolans was evident; partly due to a significantly reduced relative concentration of five free amino acids (arginine, leucine, lysine, phenylalanine and tyrosine) in pre-treated collembolans. These results, obtained from ecological transcriptomics and metabolomics jointly generated insight on various levels into the combined responses to a changing environment.

Multivariate modeling and prediction of breast cancer prognostic factors using MR metabolomics

Axillary lymph node status together with estrogen and progesterone receptor status are important prognostic factors in breast cancer. In this study, the potential of using MR metabolomics for prediction of these prognostic factors was evaluated. Biopsies from breast cancer patients (n = 160) were excised during surgery and analyzed by high resolution magic angle spinning MR spectroscopy (HR MAS MRS). The spectral data were preprocessed and variable stability (VAST) scaled, and training and test sets were generated using the Kennard-Stone and SPXY sample selection algorithms. The data were analyzed by partial least-squares discriminant analysis (PLS-DA), probabilistic neural networks (PNNs) and Bayesian belief networks (BBNs), and blind samples (n = 50) were predicted for verification. Estrogen and progesterone receptor status was successfully predicted from the MR spectra, and were best predicted by PLS-DA with a correct classification of 44 of 50 and 39 of 50 samples, respectively. Lymph node status was best predicted by BBN with 34 of 50 samples correctly classified, indicating a relationship between metabolic profile and lymph node status. Thus, MR profiles contain prognostic information that may be of benefit in treatment planning, and MR metabolomics may become an important tool for diagnosis of breast cancer patients.

NMR metabolomics and drug discovery

NMR is an integral component of the drug discovery process with applications in lead discovery, validation, and optimization. NMR is routinely used for fragment-based ligand affinity screens, high-resolution protein structure determination, and rapid protein-ligand co-structure modeling. Because of this inherent versatility, NMR is currently making significant contributions in the burgeoning area of metabolomics, where NMR is successfully being used to identify biomarkers for various diseases, to analyze drug toxicity and to determine a drug's in vivo efficacy and selectivity. This review describes advances in NMR-based metabolomics and discusses some recent applications.

Metabolic profiling and population screening of analgesic usage in nuclear magnetic resonance spectroscopy-based large-scale epidemiologic studies

The application of a (1)H nuclear magnetic resonance (NMR) spectroscopy-based screening method for determining the use of two widely available analgesics (acetaminophen and ibuprofen) in epidemiologic studies has been investigated. We used samples and data from the cross-sectional INTERMAP Study involving participants from Japan (n = 1145), China (n = 839), U.K. (n = 501), and the U.S. (n = 2195). An orthogonal projection to latent structures discriminant analysis (OPLS-DA) algorithm with an incorporated Monte Carlo resampling function was applied to the NMR data set to determine which spectra contained analgesic metabolites. OPLS-DA preprocessing parameters (normalization, bin width, scaling, and input parameters) were assessed systematically to identify an optimal acetaminophen prediction model. Subsets of INTERMAP spectra were examined to verify and validate the presence/absence of acetaminophen/ibuprofen based on known chemical shift and coupling patterns. The optimized and validated acetaminophen model correctly predicted 98.2%, and the ibuprofen model correctly predicted 99.0% of the urine specimens containing these drug metabolites. The acetaminophen and ibuprofen models were subsequently used to predict the presence/absence of these drug metabolites for the remaining INTERMAP specimens. The acetaminophen model identified 415 out of 8436 spectra as containing acetaminophen metabolite signals while the ibuprofen model identified 245 out of 8604 spectra as containing ibuprofen metabolite signals from the global data set after excluding samples used to construct the prediction models. The NMR-based metabolic screening strategy provides a new objective approach for evaluation of self-reported medication data and is extendable to other aspects of population xenometabolome profiling.

(13)C NMR pattern recognition techniques for the classification of Atlantic salmon (Salmo salar L.) according to their wild, farmed, and geographical origin

(13)C nuclear magnetic resonance (NMR) in combination with multivariate data analysis was used to (1) discriminate between farmed and wild Atlantic salmon ( Salmo salar L.), (2) discriminate between different geographical origins, and (3) verify the origin of market samples. Muscle lipids from 195 Atlantic salmon of known origin (wild and farmed salmon from Norway, Scotland, Canada, Iceland, Ireland, the Faroes, and Tasmania) in addition to market samples were analyzed by (13)C NMR spectroscopy and multivariate analysis. Both probabilistic neural networks (PNN) and support vector machines (SVM) provided excellent discrimination (98.5 and 100.0%, respectively) between wild and farmed salmon. Discrimination with respect to geographical origin was somewhat more difficult, with correct classification rates ranging from 82.2 to 99.3% by PNN and SVM, respectively. In the analysis of market samples, five fish labeled and purchased as wild salmon were classified as farmed salmon (indicating mislabeling), and there were also some discrepancies between the classification and the product declaration with regard to geographical origin.

Combining clinical assessment scores and in vivo MR spectroscopy neurometabolites in very low birth weight adolescents

OBJECTIVE

Very low birth weight (VLBW) survivors are at increased risk of neurological impairments that may persist into adolescence and adulthood. The aims of this study were to identify the most important clinical assessments that characterize differences between VLBW and control adolescents, and to look at the relationship between clinical assessments and the metabolites in in vivo MR spectra.

METHODS

At 14-15 years of age, 54 VLBW survivors and 64 term controls were examined clinically. Several neuropsychological and motor assessments were performed. The magnetic resonance (MR) brain spectra were acquired from volumes localized in the left frontal lobe and contained mainly white matter.

RESULTS

Probabilistic neural networks and support vector machines demonstrated that clinical assessments rendered a possibility of the classification of VLBW versus control adolescents. The most important clinical assessments in this classification were visual-motor integration, motor coordination, stroop test, full scale IQ, and grooved pegboard. Through the use of outer product analysis-partial least squares discriminant analysis on a subset of adolescents (n=36), the clinical assessments found to most strongly correlate with the spectral data were the global assessment scale, Wisconsin card sorting test, full scale IQ, grooved pegboard test, and motor coordination test. Clinical assessments that relate to spectral data may be especially dependent on an intact microstructure in frontal white matter.

Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS)

Current clinical strategy for staging and prognostication of colorectal cancer (CRC) relies mainly upon the TNM or Duke system. This clinicopathological stage is a crude prognostic guide because it reflects in part the delay in diagnosis in the case of an advanced cancer and gives little insight into the biological characteristics of the tumor. We hypothesized that global metabolic profiling (metabonomics/metabolomics) of colon mucosae would define metabolic signatures that not only discriminate malignant from normal mucosae, but also could distinguish the anatomical and clinicopathological characteristics of CRC. We applied both high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) and gas chromatography mass spectrometry (GC/MS) to analyze metabolites in biopsied colorectal tumors and their matched normal mucosae obtained from 31 CRC patients. Orthogonal partial least-squares discriminant analysis (OPLS-DA) models generated from metabolic profiles obtained by both analytical approaches could robustly discriminate normal from malignant samples (Q(2) > 0.50, Receiver Operator Characteristic (ROC) AUC >0.95, using 7-fold cross validation). A total of 31 marker metabolites were identified using the two analytical platforms. The majority of these metabolites were associated with expected metabolic perturbations in CRC including elevated tissue hypoxia, glycolysis, nucleotide biosynthesis, lipid metabolism, inflammation and steroid metabolism. OPLS-DA models showed that the metabolite profiles obtained via HR-MAS NMR could further differentiate colon from rectal cancers (Q(2)> 0.60, ROC AUC = 1.00, using 7-fold cross validation). These data suggest that metabolic profiling of CRC mucosae could provide new phenotypic biomarkers for CRC management.

Development and validation of a gas chromatography/mass spectrometry method for the metabolic profiling of human colon tissue

In this study, a gas chromatography/mass spectrometry (GC/MS) method was developed and validated for the metabolic profiling of human colon tissue. Each colon tissue sample (20 mg) was ultra-sonicated with 1 mL of a mixture of chloroform/methanol/water in the ratio of 20:50:20 (v/v/v), followed by centrifugation, collection of supernatant, drying, removal of moisture using anhydrous toluene and finally derivatization using N-methyl-N-trifluoroacetamide (MSTFA) with 1% trimethylchlorosilane (TMCS). A volume of 1 microL of the derivatized mixture was injected into the GC/MS system. A total of 53 endogenous metabolites were separated and identified in the GC/MS chromatogram, all of which were selected to evaluate the sample stability and precision of the method. Of the identified endogenous metabolites 19 belonging to diverse chemical classes and covering a wide range of the GC retention times (Rt) were selected to investigate the quantitative linearity of the method. The developed GC/MS method demonstrated good reproducibility with intra- and inter-day precision within relative standard deviation (RSD) of +/-15%. The metabolic profiles of the intact tissue were determined to be stable (100 +/- 15%) for up to 90 days at -80 degrees C. Satisfactory results were also obtained in the case of other stability-indicating studies such as freeze/thaw cycle stability, bench-top stability and autosampler stability. The developed method showed a good linear response for each of the 19 analytes tested (r(2) > 0.99). Our GC/MS metabolic profiling method was successfully applied to discriminate biopsied colorectal cancer (CRC) tissue from their matched normal tissue obtained from six CRC patients using orthogonal partial least-squares discriminant analysis [two latent variables, R(2)Y = 0.977 and Q(2) (cumulative) = 0.877].

Metabolomics-based methods for early disease diagnostics

The emerging field of metabolomics, in which a large number of small-molecule metabolites from body fluids or tissues are detected quantitatively in a single step, promises immense potential for early diagnosis, therapy monitoring and for understanding the pathogenesis of many diseases. Metabolomics methods are mostly focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry (MS). Analysis of the data from these high-resolution methods using advanced chemometric approaches provides a powerful platform for translational and clinical research and diagnostic applications. In this review, the current trends and recent advances in NMR- and MS-based metabolomics are described with a focus on the development of advanced NMR and MS methods, improved multivariate statistical data analysis and recent applications in the area of cancer, diabetes, inborn errors of metabolism and cardiovascular diseases.

Metabolic profiling studies on the toxicological effects of realgar in rats by (1)H NMR spectroscopy

The toxicological effects of realgar after intragastrical administration (1 g/kg body weight) were investigated over a 21 day period in male Wistar rats using metabonomic analysis of (1)H NMR spectra of urine, serum and liver tissue aqueous extracts. Liver and kidney histopathology examination and serum clinical chemistry analyses were also performed. (1)H NMR spectra and pattern recognition analyses from realgar treated animals showed increased excretion of urinary Kreb's cycle intermediates, increased levels of ketone bodies in urine and serum, and decreased levels of hepatic glucose and glycogen, as well as hypoglycemia and hyperlipoidemia, suggesting the perturbation of energy metabolism. Elevated levels of choline containing metabolites and betaine in serum and liver tissue aqueous extracts and increased serum creatine indicated altered transmethylation. Decreased urinary levels of trimethylamine-N-oxide, phenylacetylglycine and hippurate suggested the effects on the gut microflora environment by realgar. Signs of impairment of amino acid metabolism were supported by increased hepatic glutamate levels, increased methionine and decreased alanine levels in serum, and hypertaurinuria. The observed increase in glutathione in liver tissue aqueous extracts could be a biomarker of realgar induced oxidative injury. Serum clinical chemistry analyses showed increased levels of lactate dehydrogenase, aspartate aminotransferase, and alkaline phosphatase as well as increased levels of blood urea nitrogen and creatinine, indicating slight liver and kidney injury. The time-dependent biochemical variations induced by realgar were achieved using pattern recognition methods. This work illustrated the high reliability of NMR-based metabonomic approach on the study of the biochemical effects induced by traditional Chinese medicine.

1H NMR-based metabonomic and pattern recognition analysis for detection of oral squamous cell carcinoma

BACKGROUND

Metabonomic analysis has been increasingly used to monitor metabolic abnormalities in cells and their microenvironment in order to detect the cancer markers recently. We evaluated the feasibility of applying (1)H nuclear magnetic resonance ((1)H NMR) based metabonomic method in the early detection of the differences in the plasma from 3 groups, which were patients with oral squamous cell carcinoma (OSCC), patients with oral leukoplakia (OLK), and the healthy control group.

METHODS

(1)H NMR spectra were obtained from human plasma samples prior to spectral analysis. The obtained data were processed by both the unsupervised principal component analysis (PCA) and the supervised partial least squares discriminant analysis (PLS-DA) to find out the differences among the three groups.

RESULTS

PLS-DA analysis has revealed a good model to detect the NMR data that can differentiate the OSCC patients from the OLK patients and the controls using a test set.

CONCLUSION

The results indicated that the (1)H NMR-based metabonomic approach is a feasible and efficient method for differentiating the OSCC patient's plasma from the healthy controls. As a potential novel strategy and a more convenient technique, it deserves a further evaluation for an early detection of oral cancer.

Metabolomic signatures of inbreeding at benign and stressful temperatures in Drosophila melanogaster

While the population genetics of inbreeding is fairly well understood, the effects of inbreeding on the physiological and biochemical levels are not. Here we have investigated the effects of inbreeding on the Drosophila melanogaster metabolome. Metabolite fingerprints in males from five outbred and five inbred lines were studied by nuclear magnetic resonance spectroscopy after exposure to benign temperature, heat stress, or cold stress. In both the absence and the presence of temperature stress, metabolite levels were significantly different among inbred and outbred lines. The major effect of inbreeding was increased levels of maltose and decreased levels of 3-hydroxykynurenine and a galactoside [1-O-(4-O-(2-aminoethyl phosphate)-beta-d-galactopyranosyl)-x-glycerol] synthesized exclusively in the paragonial glands of Drosophila species, including D. melanogaster. The metabolomic effect of inbreeding at the benign temperature was related to gene expression data from the same inbred and outbred lines. Both gene expression and metabolite data indicate that fundamental metabolic processes are changed or modified by inbreeding. Apart from affecting mean metabolite levels, inbreeding led to an increased between-line variation in metabolite profiles compared to outbred lines. In contrast to previous observations revealing interactions between inbreeding and environmental stress on gene expression patterns and life-history traits, the effect of inbreeding on the metabolite profile was similar across the different temperature treatments.

Diagnosis of breast cancer using HPLC metabonomics fingerprints coupled with computational methods

The present study was focused on developing a computational procedure for analysis of the HPLC metabonomics fingerprints of human urine to distinguish between patients with breast cancer from healthy people. The predictive rate of support vector machine (SVM) based diagnosis model is 100% for training set and 93.2% for test set, respectively. Current work might have important reference values to explore the methodology of metabonomics.

1H nuclear magnetic resonance-based metabolomic characterization of wines by grape varieties and production areas

(1)H NMR spectroscopy was used to investigate the metabolic differences in wines produced from different grape varieties and different regions. A significant separation among wines from Campbell Early, Cabernet Sauvignon, and Shiraz grapes was observed using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The metabolites contributing to the separation were assigned to be 2,3-butanediol, lactate, acetate, proline, succinate, malate, glycerol, tartarate, glucose, and phenolic compounds by PCA and PLS-DA loading plots. Wines produced from Cabernet Sauvignon grapes harvested in the continental areas of Australia, France, and California were also separated. PLS-DA loading plots revealed that the level of proline in Californian Cabernet Sauvignon wines was higher than that in Australian and French Cabernet Sauvignon, Australian Shiraz, and Korean Campbell Early wines, showing that the chemical composition of the grape berries varies with the variety and growing area. This study highlights the applicability of NMR-based metabolomics with multivariate statistical data sets in determining wine quality and product origin.

Analysis of metabolomic data using support vector machines

Metabolomics is an emerging field providing insight into physiological processes. It is an effective tool to investigate disease diagnosis or conduct toxicological studies by observing changes in metabolite concentrations in various biofluids. Multivariate statistical analysis is generally employed with nuclear magnetic resonance (NMR) or mass spectrometry (MS) data to determine differences between groups (for instance diseased vs healthy). Characteristic predictive models may be built based on a set of training data, and these models are subsequently used to predict whether new test data falls under a specific class. In this study, metabolomic data is obtained by doing a (1)H NMR spectroscopy on urine samples obtained from healthy subjects (male and female) and patients suffering from Streptococcus pneumoniae. We compare the performance of traditional PLS-DA multivariate analysis to support vector machines (SVMs), a technique widely used in genome studies on two case studies: (1) a case where nearly complete distinction may be seen (healthy versus pneumonia) and (2) a case where distinction is more ambiguous (male versus female). We show that SVMs are superior to PLS-DA in both cases in terms of predictive accuracy with the least number of features. With fewer number of features, SVMs are able to give better predictive model when compared to that of PLS-DA.

Multilevel data analysis of a crossover designed human nutritional intervention study

A new method is introduced for the analysis of 'omics' data derived from crossover designed drug or nutritional intervention studies. The method aims at finding systematic variations in metabolic profiles after a drug or nutritional challenge and takes advantage of the crossover design in the data. The method, which can be considered as a multivariate extension of a paired t test, generates different multivariate submodels for the between- and the within-subject variation in the data. A major advantage of this variation splitting is that each submodel can be analyzed separately without being confounded with the other variation sources. The power of the multilevel approach is demonstrated in a human nutritional intervention study which used NMR-based metabolomics to assess the metabolic impact of grape/wine extract consumption. The variations in the urine metabolic profiles are studied between and within the human subjects using the multilevel analysis. After variation splitting, multilevel PCA is used to investigate the experimental and biological differences between the subjects, whereas a multilevel PLS-DA model is used to reveal the net treatment effect within the subjects. The observed treatment effect is validated with cross model validation and permutations. It is shown that the statistical significance of the multilevel classification model ( p << 0.0002) is a major improvement compared to a ordinary PLS-DA model ( p = 0.058) without variation splitting. Finally, rank products are used to determine which NMR signals are most important in the multilevel classification model.

Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods

Multivariate metabolic profiles from biofluids such as urine and plasma are highly indicative of the biological fitness of complex organisms and can be captured analytically in order to derive top-down systems biology models. The application of currently available modeling approaches to human and animal metabolic pathway modeling is problematic because of multicompartmental cellular and tissue exchange of metabolites operating on many time scales. Hence, novel approaches are needed to analyze metabolic data obtained using minimally invasive sampling methods in order to reconstruct the patho-physiological modulations of metabolic interactions that are representative of whole system dynamics. Here, we show that spectroscopically derived metabolic data in experimental liver injury studies (induced by hydrazine and alpha-napthylisothiocyanate treatment) can be used to derive insightful probabilistic graphical models of metabolite dependencies, which we refer to as metabolic interactome maps. Using these, system level mechanistic information on homeostasis can be inferred, and the degree of reversibility of induced lesions can be related to variations in the metabolic network patterns. This approach has wider application in assessment of system level dysfunction in animal or human studies from noninvasive measurements.

Quantitative analysis of NMR spectra with chemometrics

The number of applications of chemometrics to series of NMR spectra is rapidly increasing due to an emerging interest for quantitative NMR spectroscopy e.g. in the pharmaceutical and food industries. This paper gives an analysis of advantages and limitations of applying the two most common chemometric procedures, Principal Component Analysis (PCA) and Multivariate Curve Resolution (MCR), to a designed set of 231 simple alcohol mixture (propanol, butanol and pentanol) (1)H 400 MHz spectra. The study clearly demonstrates that the major advantage of chemometrics is the visualisation of larger data structures which adds a new exploratory dimension to NMR research. While robustness and powerful data visualisation and exploration are the main qualities of the PCA method, the study demonstrates that the bilinear MCR method is an even more powerful method for resolving pure component NMR spectra from mixtures when certain conditions are met.

Toxicological effects of cinnabar in rats by NMR-based metabolic profiling of urine and serum

Cinnabar, an important traditional Chinese mineral medicine, has been widely used as a Chinese patent medicine ingredient for sedative therapy. However, the pharmaceutical and toxicological effects of cinnabar, especially in the whole organism, were subjected to few investigations. In this study, an NMR-based metabolomics approach has been applied to investigate the toxicological effects of cinnabar after intragastrical administration (dosed at 0.5, 2 and 5 g/kg body weight) on male Wistar rats. Liver and kidney histopathology examinations and serum clinical chemistry analyses were also performed. The 1H NMR spectra were analyzed using multivariate pattern recognition techniques to show the time- and dose-dependent biochemical variations induced by cinnabar. The metabolic signature of urinalysis from cinnabar-treated animals exhibited an increase in the levels of creatinine, acetate, acetoacetate, taurine, hippurate and phenylacetylglycine, together with a decrease in the levels of trimethyl-N-oxide, dimethylglycine and Kreb's cycle intermediates (citrate, 2-oxoglutarate and succinate). The metabolomics analyses of serum showed elevated concentrations of ketone bodies (3-d-hydroxybutyrate and acetoacetate), branched-chain amino acids (valine, leucine and isoleucine), choline and creatine as well as decreased glucose, lipids and lipoproteins from cinnabar-treated animals. These findings indicated cinnabar induced disturbance in energy metabolism, amino acid metabolism and gut microflora environment as well as slight injury in liver and kidney, which might indirectly result from cinnabar induced oxidative stress. This work illustrated the high reliability of NMR-based metabolomic approach on the study of the biochemical effects induced by traditional Chinese medicine.

1H NMR based metabolic profiling in the evaluation of Japanese green tea quality

Classification of tea quality is now mainly performed according to the sensory results by professional tea tasters. However, this evaluation method is inconsistent in differentiating their qualities. A combination of a (1)H NMR technique and a multivariate analysis was introduced to the quality evaluation of green tea by means of a metabolomic technique. A broad range of metabolites were detected by (1)H NMR spectrometry. The principal component analysis (PCA) was used to reduce the complexity of the (1)H NMR spectra data set and provided the quality discrimination result. It offered an extensive clue for classification and quality assessment without any prepurification method. A set of green teas from a Japanese tea contest were analyzed by (1)H NMR to classify the quality with respect to that judged by tea tasters and to conceive a quality prediction model. Metabolic profiling and fingerprinting of (1)H NMR spectra of green teas with different quality were studied. PCA showed a separation between the high- and the low-quality green teas. The taste marker compounds contributing to the discrimination of tea quality were identified. Reliable prediction models were obtained by the partial least-squares projection to latent structure (PLS) analysis together with a preprocessing filter of both orthogonal signal correction (OSC) and a combination between OSC and wavelet transform algorithms.

Prediction and classification of drug toxicity using probabilistic modeling of temporal metabolic data: the consortium on metabonomic toxicology screening approach

Detection and classification of in vivo drug toxicity is an expensive and time-consuming process. Metabolic profiling is becoming a key enabling tool in this area as it provides a unique perspective on the characterization and mechanisms of response to toxic insult. As part of the Consortium on Metabonomic Toxicology (COMET) project, a substantial metabolic and pathological database was constructed. We chose a set of 80 treatments to build a modeling system for toxicity prediction using NMR spectroscopy of urine samples (n=12935) from laboratory rats (n=1652). The compound structures and activities were diverse but there was an emphasis on the selection of hepato and nephrotoxins. We developed a two-stage strategy based on the assumptions that (a) adverse effects would produce metabolic profiles deviating from those of normal animals and (b) such deviations would be similar for treatments having similar physiological effects. To address the first stage, we developed a multivariate model of normal urine, using principal components analysis of specially preprocessed 1H NMR spectra. The model demonstrated a high correspondence between the occurrence of toxicity and abnormal metabolic profiles. In the second stage, we extended a density estimation method, "CLOUDS", to compute multidimensional similarities between treatments. Crucially, the technique allowed a distribution-free estimate of similarity across multiple animals and time points for each treatment and the resulting matrix of similarities showed segregation between liver toxins and other treatments. Using the similarity matrix, we were able to correctly identify the target organ of two "blind" treatments, even at sub-toxic levels. To further validate the approach, we then applied a leave-one-out approach to predict the main organ of toxicity (liver or kidney) showing significant responses using the three most similar matches in the matrix. Where predictions could be made, there was an error rate of 8%. The sensitivities to liver and kidney toxicity were 67 and 41%, respectively, whereas the corresponding specificities were 77 and 100%. In some cases, it was not possible to make predictions because of interference by drug-related metabolite signals (18%), an inconsistent histopathological or urinary response (11%), genuine class overlap (8%), or lack of similarity to any other treatment (2%). This study constitutes the largest validation to date of the metabonomic approach to preclinical toxicology assessment, confirming that the methodology offers practical utility for rapid in vivo drug toxicity screening.

1H NMR study of the effects of sample contamination in the metabolomic analysis of mouse urine

Nuclear magnetic resonance (NMR) spectroscopy was used to evaluate and optimize the strategy for collecting mouse urine samples. A series of normal urine samples and those mixed with folate-deficient food, turkey or mouse fecal particles were analyzed using principal component analysis (PCA). The metabolic profile of urine mixed with folate-deficient food was found to be extremely different than that of clean urine. Changes in the urine composition caused by mixing with turkey or feces are relatively small as judged by the output of PCA. As a result, turkey may be considered as an applicable food source for obtaining uncontaminated urine samples for metabolomics-based research.

Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation

Background

Classifying nuclear magnetic resonance (NMR) spectra is a crucial step in many metabolomics experiments. Since several multivariate classification techniques depend upon the variance of the data, it is important to first minimise any contribution from unwanted technical variance arising from sample preparation and analytical measurements, and thereby maximise any contribution from wanted biological variance between different classes. The generalised logarithm (glog) transform was developed to stabilise the variance in DNA microarray datasets, but has rarely been applied to metabolomics data. In particular, it has not been rigorously evaluated against other scaling techniques used in metabolomics, nor tested on all forms of NMR spectra including 1-dimensional (1D) ¹H, projections of 2D ¹H, ¹H J-resolved (pJRES), and intact 2D J-resolved (JRES).

Results

Here, the effects of the glog transform are compared against two commonly used variance stabilising techniques, autoscaling and Pareto scaling, as well as unscaled data. The four methods are evaluated in terms of the effects on the variance of NMR metabolomics data and on the classification accuracy following multivariate analysis, the latter achieved using principal component analysis followed by linear discriminant analysis. For two of three datasets analysed, classification accuracies were highest following glog transformation: 100% accuracy for discriminating 1D NMR spectra of hypoxic and normoxic invertebrate muscle, and 100% accuracy for discriminating 2D JRES spectra of fish livers sampled from two rivers. For the third dataset, pJRES spectra of urine from two breeds of dog, the glog transform and autoscaling achieved equal highest accuracies. Additionally we extended the glog algorithm to effectively suppress noise, which proved critical for the analysis of 2D JRES spectra.

Conclusion

We have demonstrated that the glog and extended glog transforms stabilise the technical variance in NMR metabolomics datasets. This significantly improves the discrimination between sample classes and has resulted in higher classification accuracies compared to unscaled, autoscaled or Pareto scaled data. Additionally we have confirmed the broad applicability of the glog approach using three disparate datasets from different biological samples using 1D NMR spectra, 1D projections of 2D JRES spectra, and intact 2D JRES spectra.

Application of metabonomics in drug development

Metabolic profiling (metabonomics/metabolomics) is the untargeted analysis of metabolic composition in a biological sample, and is principally aimed at biomarker discovery. The frequent use of noninvasive biofluid analysis in metabonomics is suited to the clinic and facilitates dynamic monitoring. Analytical protocols for metabolic biomarkers are potentially robust because a metabolite is the same chemical entity irrespective of its origin, facilitating ‘bench-to-bedside’ translational research. Metabonomics can make an impact at several points in the drug-development process: target identification; lead discovery and optimization; preclinical efficacy and safety assessment; mode-of-action and mechanistic toxicology; patient stratification; and clinical pharmacological monitoring. This review describes and exemplifies the latest developments in each of these areas, including the impact of new data and chemical analytical techniques. The future goals for metabonomics are the validation of existing biomarkers, in terms of mechanism and translation to man, together with a focus on characterizing the individual (‘personalized healthcare’).