Automatic data reduction and pattern recognition methods for analysis of 1H nuclear magnetic resonance spectra of human urine from normal and pathological states

Pretreating and normalizing metabolomics data for statistical analysis

Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples. Metabolomics is emerging as a powerful tool generally for precision medicine. Particularly, integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease. However, metabolomics data are very complicated. Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis. In this review article, we comprehensively review various methods that are used to preprocess and pretreat metabolomics data, including MS-based data and NMR -based data preprocessing, dealing with zero and/or missing values and detecting outliers, data normalization, data centering and scaling, data transformation. We discuss the advantages and limitations of each method. The choice for a suitable preprocessing method is determined by the biological hypothesis, the characteristics of the data set, and the selected statistical data analysis method. We then provide the perspective of their applications in the microbiome and metabolome research.

Metabolomic diferences between COVID-19 and H1N1 influenza induced ARDS

BACKGROUND

Acute respiratory distress syndrome (ARDS) is a type of respiratory failure characterized by lung inflammation and pulmonary edema. Coronavirus disease 2019 (COVID-19) is associated with ARDS in the more severe cases. This study aimed to compare the specificity of the metabolic alterations induced by COVID-19 or Influenza A pneumonia (IAP) in ARDS.

METHODS

Eighteen patients with ARDS due to COVID-19 and twenty patients with ARDS due to IAP, admitted to the intensive care unit. ARDS was defined as in the American-European Consensus Conference. As compared with patients with COVID-19, patients with IAP were younger and received more often noradrenaline to maintain a mean arterial pressure > 65 mm Hg. Serum samples were analyzed by Nuclear Magnetic Resonance Spectroscopy. Multivariate Statistical Analyses were used to identify metabolic differences between groups. Metabolic pathway analysis was performed to identify the most relevant pathways involved in ARDS development.

RESULTS

ARDS due to COVID-19 or to IAP induces a different regulation of amino acids metabolism, lipid metabolism, glycolysis, and anaplerotic metabolism. COVID-19 causes a significant energy supply deficit that induces supplementary energy-generating pathways. In contrast, IAP patients suffer more marked inflammatory and oxidative stress responses. The classificatory model discriminated against the cause of pneumonia with a success rate of 100%.

CONCLUSIONS

Our findings support the concept that ARDS is associated with a characteristic metabolomic profile that may discriminate patients with ARDS of different etiologies, being a potential biomarker for the diagnosis, prognosis, and management of this condition.

Changes in the Salivary Metabolic Profile of Generalized Periodontitis Patients after Non-surgical Periodontal Therapy: A Metabolomic Analysis Using Nuclear Magnetic Resonance Spectroscopy

Pattern analysis of the salivary metabolic profile has been proven accurate in discriminating between generalized periodontitis (GP) patients and healthy individuals (HI), as this disease modifies the salivary concentrations of specific metabolites. Due to the scarcity of data from previous studies, this study aimed to evaluate if non-surgical periodontal therapy (NST) could affect the metabolomic profile in GP patients’ saliva and if it compares to that of HI. Unstimulated salivary samples were collected from 11 HI and 12 GP patients before and 3 months after NST. Nuclear Magnetic Resonance (NMR) spectroscopy, followed by a supervised multivariate statistical approach on entire saliva spectra and partial least square (PLS) discriminant analysis, were performed to obtain metabolic profiles. In the GP group, periodontal treatment improved all clinical parameters, but not all the diseased sites were eradicated. PLS revealed an accuracy of 100% in distinguishing between metabolic profiles of GP patients before and after NST. Orthogonal projection to latent structure was able to discriminate between the three groups of subjects with an accuracy of 85.6%. However, the post-NST metabolic profile of GP patients could not be completely assimilated to that of HI. Although NST may produce significant changes in the metabolic profile, GP patients maintained a distinctive fingerprint compared to HI.

pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra

Motivation

Data processing is a key bottleneck for ¹H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D ¹H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA (“pJRES Binning Algorithm”), which aims to extend the applicability of SRV to pJRES spectra.

Results

The performance of JBA is comprehensively evaluated using 617 plasma ¹H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building

Availability and implementation

The algorithm is implemented using the MWASTools R/Bioconductor package.

Supplementary information

Supplementary data are available at Bioinformatics online.

Nuclear magnetic resonance (NMR)-based metabolome profile evaluation in dairy cows with and without displaced abomasum

Background

Displaced abomasum (DA) is a condition of dairy cows that severely impacts animal welfare and causes huge economic losses.

Objective

To assess the metabolic status of the disease using metabolomics in serum, urine and liver samples aimed at both water soluble and lipid soluble fractions.

Methods

Fifty Holstein multiparous cows with DA (42 left, 8 right) and 20 clinically healthy Holstein multiparous cows were used. Left DA was associated with concomitant ketosis in 19 animals and right in two. NMR-based metabolomics approach and hematological and biochemical analyses were performed. Statistical analysis was carried out on ¹H-NMR data after they have been normalized using PQN method.

Results

Contrary to generated PCA score plots the OPLS-supervised method revealed differences between healthy animals and diseased ones based on serum water-soluble samples. While water and lipid soluble metabolites decreased in serum samples, fatty acid fractions and cholesterol were increased in liver samples in DA affected cows. The metabolomic and chemical profiles clearly revealed that cows with DA (especially with LDA) were at risk of ketosis and fatty liver. Serum hippuric acid concentration was significantly higher in healthy cows in comparison with LDA, whereas serum glycine concentration was reported higher for healthy when compared to RDA affected animals.

Conclusion

A biochemical network and pathway mapping revealed ‘valine, leucine and isoleucine biosynthesis’ and ‘phenylalanine, tyrosine and tryptophan biosynthesis’ as the most probable altered metabolic pathway in DA condition. Serum was advocated as the optimal biological matrix for the ¹H-NMR analysis.

NMR-Based Metabolomics for the Assessment of Inhaled Pharmacotherapy in Chronic Obstructive Pulmonary Disease Patients

The aim of this proof-of-concept, pilot study was the evaluation of the effects of steroid administration and suspension of an inhaled corticosteroid (ICS)-long-acting β₂-agonist (LABA) extrafine fixed dose combination (FDC) on metabolomic fingerprints in subjects with chronic obstructive pulmonary disease (COPD). We hypothesized that a comprehensive metabolomics approach discriminates across inhaled pharmacotherapies and that their effects on metabolomic signatures depend on the biological fluids analyzed. We performed metabolomics via nuclear magnetic resonance (NMR) spectroscopy in exhaled breath condensate (EBC), sputum supernatants, serum, and urine. Fourteen patients suffering from COPD who were on regular inhaled fluticasone propionate/salmeterol therapy (visit 1) were consecutively treated with 2-week beclomethasone dipropionate/formoterol (visit 2), 4-week formoterol alone (visit 3), and 4-week beclomethasone/formoterol (visit 4). The comprehensive NMR-based metabolomics approach showed differences across all pharmacotherapies and that different biofluids provided orthogonal information. Serum formate was lower at visits 1 versus 3 (P = 0.03), EBC formate was higher at visit 1 versus 4 (P = 0.03), and urinary 1-methyl-nicotinamide was lower at 3 versus 4 visit (P = 0.002). NMR-based metabolomics of different biofluids distinguishes across inhaled pharmacotherapies, provides complementary information on the effects of an extrafine ICS/LABA FDC on metabolic fingerprints in COPD patients, and might be useful for elucidating the ICS mechanism of action.

Metabolomic analysis of serum may refine 21-gene expression assay risk recurrence stratification

Despite recent refinements to the 21-gene g score, allowing a better identification of patients who may derive no benefit from the addition of adjuvant chemotherapy to that of endocrine therapy, patients with early breast cancer still stand to be over-treated in the setting of clinical and/or genomic uncertainty or discordance. Here we describe and demonstrate a potential approach of further refining the OncotypeDX risk score by metabolomic analysis of serum. In a clinical dataset (N = 87), the risk of recurrence was further sub-stratified by metabolomic signature, with an effective splitting of each Oncotype risk classification. A total of seven recurrences were recorded, with metabolomic analysis accurately predicting six of these. Contrastingly, the genomic risk score of the seven recurrences ranged across all three Oncotype classifications (one recurrence occurred in the "low"-risk group, three in the "intermediate" group and three in the "high"-risk group).

The metabolic fingerprints of HCV and HBV infections studied by Nuclear Magnetic Resonance Spectroscopy

Few studies are available on metabolic changes in liver injuries and this is the first metabolomic study evaluating a group of HCV-positive patients, before and after viral eradication via DAA IFN-free regimens, using ¹H-NMR to characterize and compare their serum fingerprints to naïve HBV-patients and healthy donors. The investigation clearly shows differences in the metabolomic profile of HCV patients before and after effective DAA treatment. Significant changes in metabolites levels in patients undergoing therapy suggest alterations in several metabolic pathways. It has been shown that ¹H-NMR fingerprinting approach is an optimal technique in predicting the specific infection and the healthy status of studied subjects (Monte-Carlo cross validated accuracies: 86% in the HCV vs HBV model, 98.7% in the HCV vs HC model). Metabolite data collected support the hypothesis that the HCV virus induces glycolysis over oxidative phosphorylation in a similar manner to the Warburg effect in cancer, moreover our results have demonstrated a different action of the two viruses on cellular metabolism, corroborating the hypothesis that the metabolic perturbation on patients could be attributed to a direct role in viral infection. This metabolomic study has revealed some alteration in metabolites for the first time (2-oxoglutarate and 3-hydroxybutrate) concerning the HCV-infection model that could explain several extrahepatic manifestations associated with such an infection.

Network analysis of membranous glomerulonephritis based on metabolomics data

Membranous glomerulonephritis (MGN) is one of the most frequent causes of nephrotic syndrome in adults. It is characterized by the thickening of the glomerular basement membrane in the renal tissue. The current diagnosis of MGN is based on renal biopsy and the detection of antibodies to the few podocyte antigens. Due to the limitations of the current diagnostic methods, including invasiveness and the lack of sensitivity of the current biomarkers, there is a requirement to identify more applicable biomarkers. The present study aimed to identify diagnostic metabolites that are involved in the development of the disease using topological features in the component‑reaction‑enzyme‑gene (CREG) network for MGN. Significant differential metabolites in MGN compared with healthy controls were identified using proton nuclear magnetic resonance and gas chromatography‑mass spectrometry techniques, and multivariate analysis. The CREG network for MGN was constructed, and metabolites with a high centrality and a striking fold‑change in patients, compared with healthy controls, were introduced as putative diagnostic biomarkers. In addition, a protein‑protein interaction (PPI) network, which was based on proteins associated with MGN, was built and analyzed using PPI analysis methods, including molecular complex detection and ClueGene Ontology. A total of 26 metabolites were identified as hub nodes in the CREG network, 13 of which had salient centrality and fold‑changes: Dopamine, carnosine, fumarate, nicotinamide D‑ribonucleotide, adenosine monophosphate, pyridoxal, deoxyguanosine triphosphate, L‑citrulline, nicotinamide, phenylalanine, deoxyuridine, tryptamine and succinate. A total of 13 subnetworks were identified using PPI analysis. In total, two of the clusters contained seed proteins (phenylalanine‑4‑hydroxlylase and cystathionine γ‑lyase) that were associated with MGN based on the CREG network. The following biological processes associated with MGN were identified using gene ontology analysis: 'Pyrimidine‑containing compound biosynthetic process', 'purine ribonucleoside metabolic process', 'nucleoside catabolic process', 'ribonucleoside metabolic process' and 'aromatic amino acid family metabolic process'. The results of the present study may be helpful in the diagnostic and therapeutic procedures of MGN. However, validation is required in the future.

Identification of novel metabolomic biomarkers in an experimental model of septic acute kidney injury

The aim of this study is the identification of metabolomic biomarkers of sepsis and sepsis-induced acute kidney injury (AKI) in an experimental model. Pigs were anesthetized and monitored to measure mean arterial pressure (MAP), systemic blood flow (Q_T), mean pulmonary arterial pressure, renal artery blood flow (Q_RA), renal cortical blood flow (Q_RC), and urine output (UO). Sepsis was induced at t = 0 min by the administration of live Escherichia coli ( n = 6) or saline ( n = 8). At t = 300 min, animals were killed. Renal tissue, urine, and serum samples were analyzed by nuclear magnetic resonance (NMR) spectroscopy. Principal component analyses were performed on the processed NMR spectra to highlight kidney injury biomarkers. Sepsis was associated with decreased Q_T and MAP and decreased Q_RA, Q_RC, and UO. Creatinine serum concentration and neutrophil gelatinase-associated lipocalin (NGAL) serum and urine concentrations increased. NMR-based metabolomics analysis found metabolic differences between control and septic animals: 1) in kidney tissue, increased lactate and nicotinuric acid and decreased valine, aspartate, glucose, and threonine; 2) in urine, increased isovaleroglycine, aminoadipic acid, N-acetylglutamine, N-acetylaspartate, and ascorbic acid and decreased myoinositol and phenylacetylglycine; and 3) in serum, increased lactate, alanine, pyruvate, and glutamine and decreased valine, glucose, and betaine concentrations. The concentration of several metabolites altered in renal tissue and urine samples from septic animals showed a significant correlation with markers of AKI (i.e., creatinine and NGAL serum concentrations). NMR-based metabolomics is a potentially useful tool for biomarker identification of sepsis-induced AKI.

Effect of non-surgical periodontal therapy on salivary metabolic fingerprint of generalized chronic periodontitis using nuclear magnetic resonance spectroscopy

OBJECTIVE

Metabolomic analysis of saliva proved its accuracy in discriminating patients with generalized chronic periodontitis (GCP) from healthy subjects by identifying specific molecular signatures of the disease. There is lack of investigations concerning the effect of periodontal treatment on individual metabolic fingerprints. Therefore, the aim of this study was to determine whether non-surgical periodontal therapy could change salivary metabolomic profile in GCP to one more similar to periodontal health.

DESIGN

Unstimulated whole saliva of 32 controls and 19 GCP patients were obtained prior to and 3 months after conventional staged non-surgical periodontal therapy. Metabolic profiling was performed using Nuclear Magnetic Resonance (NMR) spectroscopy, followed by univariate and multivariate paired approaches to assess the changes introduced by the therapy.

RESULTS

In GCP group, periodontal treatment led to an improvement in all clinical parameters (p < 0.001). The accuracy of the multivariate model in discriminating the metabolomic profile of each GCP patient at two time points was 92.5%. Despite the almost perfect separation of the spectra in the metabolic space, the univariate analysis failed to identify significant variations in single metabolite content. The post-treatment metabolic profile of GCP patients could not be assimilated to that of healthy controls who exhibited different levels of lactate, pyruvate, valine, proline, tyrosine, and formate.

CONCLUSIONS

Based on these data, NMR-spectroscopic analysis revealed that, despite significant changes in the overall metabolomic fingerprint after non-surgical therapy, GCP patients maintained a distinctive metabolic profile compared to healthy individuals.

Analysis of salivary phenotypes of generalized aggressive and chronic periodontitis through nuclear magnetic resonance-based metabolomics

BACKGROUND

Recent findings about the differential gene expression signature of periodontal lesions have raised the hypothesis of distinctive biological phenotypes expressed by generalized chronic periodontitis (GCP) and generalized aggressive periodontitis (GAgP) patients. Therefore, this cross-sectional investigation was planned, primarily, to determine the ability of nuclear magnetic resonance (NMR) spectroscopic analysis of unstimulated whole saliva to discriminate GCP and GAgP disease-specific metabolomic fingerprint and, secondarily, to assess potential metabolites discriminating periodontitis patients from periodontally healthy individuals (HI).

METHODS

NMR-metabolomics spectra were acquired from salivary samples of patients with a clinical diagnosis of GCP (n = 33) or GAgP (n = 28) and from HI (n = 39). The clustering of HI, GCP, and GAgP patients was achieved by using a combination of the Principal Component Analysis and Canonical Correlation Analysis on the NMR profiles.

RESULTS

These analyses revealed a significant predictive accuracy discriminating HI from GCP, and discriminating HI from GAgP patients (both 81%). In contrast, the GAgP and GCP saliva samples seem to belong to the same metabolic space (60% predictive accuracy). Significantly lower levels (P < 0.05) of pyruvate, N-acetyl groups and lactate and higher levels (P < 0.05) of proline, phenylalanine, and tyrosine were found in GCP and GAgP patients compared with HI.

CONCLUSIONS

Within the limitations of this study, CGP and GAgP metabolomic profiles were not unequivocally discriminated through a NMR-based spectroscopic analysis of saliva.

NMR-Based Plasma Metabolomics at Set Intervals in Newborn Dairy Calves with Severe Sepsis

The aim of this first study was to reveal the new potential biomarkers by a metabolomics approach in severe septic calves. Sepsis is a common cause of morbidity and mortality in newborn dairy calves. The main challenges with the use of biomarkers of sepsis in domestic animals are their availability, cost, and time required to obtain a result. Metabolomics may offer the potential to identify biomarkers that define calf sepsis in terms of combined clinical, physiological, and pathobiological abnormalities. To our knowledge, this is the first study presenting an NMR- (nuclear magnetic resonance-) based plasma metabolomics at set intervals in neonatal septic calves. Twenty neonatal dairy calves with severe sepsis and ten healthy calves were used. Hematological and biochemical health profiles were gathered in plasma samples at set intervals. Similarly, NMR spectra were acquired. All diseased animals (except one) died after 72 hours. Clinical and laboratory results were in accordance with those of severe septic animals. Multivariate analysis on NMR plasma spectra proved to be an excellent tool for faster identification of calves with severe sepsis from healthy animals. The NMR-based metabolomic profile may contribute to the better understanding of severe sepsis in newborn calves.

Resolving NMR signals of short-chain fatty acid mixtures using unsupervised component analysis

Nuclear magnetic resonance (NMR) is a very powerful instrumental technique suited to identify and characterize organic compounds. NMR has been successfully used in the analysis of complex biological and environmental samples; however, these applications are still rather limited. In this work, we describe unsupervised component analysis as a multivariate unsupervised method suited to identify the number of relevant NMR signal contributions and to deconvolve mixed signals into signal individual sources and respective contributions. Using this approach, we were able to advance further in the field of quantification of NMR spectra, and this methodology will help in the characterization of complex biological samples. Copyright © 2017 John Wiley & Sons, Ltd.

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites' reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

Nuclear magnetic resonance metabolomics and human liver diseases: The principles and evidence associated with protein and carbohydrate metabolism

During the last decade, metabolomics has become widely used in the field of human diseases. Numerous studies have demonstrated that this is a powerful technique for improving the understanding, diagnosis and management of various types of liver disease, such as acute and chronic liver diseases, and liver transplantation. Nuclear magnetic resonance (NMR) spectroscopy is one of the two most commonly applied methods for metabolomics. The aim of the present review was to investigate the results from recent key publications focusing on aspects of protein and carbohydrate metabolism. The review includes existing procedures, which are currently used for NMR data acquisition and statistical analysis. In addition, notable results obtained by these studies on protein and carbohydrate metabolism concerning human liver diseases are presented.

Pattern recognition analysis of proton nuclear magnetic resonance spectra of postmortem cerebrospinal fluid from rats with drug-induced seizure or coma

Cerebrospinal fluid (CSF) is routinely subjected to gross evaluation in postmortem investigations; however, its use in chemical evaluations has not been fully realized. Analysis of nuclear magnetic resonance (NMR) spectra with pattern recognition methods was applied to CSF samples. Rats were treated with pentylenetetrazol (PTZ) to induce seizure or pentobarbital (PB) to induce coma, and postmortem CSF was collected after CO₂ gas euthanization. Pattern recognition analysis of the NMR data was performed on individual postmortem CSF samples. The aim of this study was to determine if pattern recognition analysis of NMR data could be used to classify the rats according to their drug treatment. The applicability of NMR data with pattern recognition analysis using postmortem CSF was also assessed. Partial Least Squares-Discriminant Analysis (PLS-DA) score plots indicated that the PTZ, PB, and NS (control) groups were clustered and clearly separated. PLS-DA correlation loading plots showed respective spectral and category variances of 41% and 42% for factor 1, and 17% and 27% for factor 2. Thus, factors 1 and 2 together described 58% (41%+17%) and 69% (42%+27%) of the variation, respectively. NMR study of postmortem CSF has the potential to be utilized as both a novel forensic neurochemistry method and in the clinical setting.

Serum Metabolomic Profiles Identify ER-Positive Early Breast Cancer Patients at Increased Risk of Disease Recurrence in a Multicenter Population

Purpose: Detecting signals of micrometastatic disease in patients with early breast cancer (EBC) could improve risk stratification and allow better tailoring of adjuvant therapies. We previously showed that postoperative serum metabolomic profiles were predictive of relapse in a single-center cohort of estrogen receptor (ER)-negative EBC patients. Here, we investigated this further using preoperative serum samples from ER-positive, premenopausal women with EBC who were enrolled in an international phase III trial.Experimental Design: Proton nuclear magnetic resonance (NMR) spectroscopy of 590 EBC samples (319 with relapse or ≥6 years clinical follow-up) and 109 metastatic breast cancer (MBC) samples was performed. A Random Forest (RF) classification model was built using a training set of 85 EBC and all MBC samples. The model was then applied to a test set of 234 EBC samples, and a risk of recurrence score was generated on the basis of the likelihood of the sample being misclassified as metastatic.Results: In the training set, the RF model separated EBC from MBC with a discrimination accuracy of 84.9%. In the test set, the RF recurrence risk score correlated with relapse, with an AUC of 0.747 in ROC analysis. Accuracy was maximized at 71.3% (sensitivity, 70.8%; specificity, 71.4%). The model performed independently of age, tumor size, grade, HER2 status and nodal status, and also of Adjuvant! Online risk of relapse score.Conclusions: In a multicenter group of EBC patients, we developed a model based on preoperative serum metabolomic profiles that was prognostic for disease recurrence, independent of traditional clinicopathologic risk factors. Clin Cancer Res; 23(6); 1422-31. ©2017 AACR.

NMR and MS Methods for Metabolomics

Metabolomics, also often referred as "metabolic profiling," is the systematic profiling of metabolites in biofluids or tissues of organisms and their temporal changes. In the last decade, metabolomics has become more and more 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 metabolomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabolomics, i.e., NMR, 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 metabolomics 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.

A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments

Metabonomics/metabolomics is an important science for the understanding of biological systems and the prediction of their behaviour, through the profiling of metabolites. Two technologies are routinely used in order to analyse metabolite profiles in biological fluids: nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), the latter typically with hyphenation to a chromatography system such as liquid chromatography (LC), in a configuration known as LC-MS. With both NMR and MS-based detection technologies, the identification of the metabolites in the biological sample remains a significant obstacle and bottleneck. This article provides guidance on methods for metabolite identification in biological fluids using NMR spectroscopy, and is illustrated with examples from recent studies on mice.

Monitoring the Response of the Human Urinary Metabolome to Brief Maximal Exercise by a Combination of RP-UPLC-MS and (1)H NMR Spectroscopy

The delineation of exercise biochemistry by utilizing metabolic fingerprinting has become an established strategy. We present a combined RP-UPLC-MS and (1)H NMR strategy, supplemented by photometric assays, to monitor the response of the human urinary metabolome to short maximal exercise. Seventeen male volunteers performed two identical sprint sessions on separate days, consisting of three 80 m maximal runs. Using univariate and multivariate analyses, we followed the fluctuation of 37 metabolites at 1, 1.5, and 2 h postexercise. 2-Hydroxyisovalerate, 2-hydroxybutyrate, 2-oxoisocaproate, 3-methyl-2-oxovalerate, 3-hydroxyisobutyrate, 2-oxoisovalerate, 3-hydroxybutyrate, 2-hydroxyisobutyrate, alanine, pyruvate, and fumarate increased 1 h postexercise and then returned toward baseline. Lactate and acetate were higher than baseline at 1 and 1.5 h. Hypoxanthine and inosine remained above baseline throughout the postexercise period. Urate decreased at 1 h and increased at 1.5 h before returning to baseline. Valine, isoleucine, succinate, citrate, trimethylamine, trimethylamine N-oxide, tyrosine, and formate decreased at 1 h and/or 1.5 h postexercise and then returned to baseline. Creatinine gradually decreased over the sampling period. Glycine, 4-aminohippurate, and hippurate remained below baseline throughout the postexercise period. Our findings show that even one-half minute of maximal exercise elicited major perturbations in human metabolism, several of which persisted for at least 2 h.

1H nuclear magnetic resonance-based extracellular metabolomic analysis of multidrug resistant Tca8113 oral squamous carcinoma cells

A major obstacle of successful chemotherapy is the development of multidrug resistance (MDR) in the cancer cells, which is difficult to reverse. Metabolomic analysis, an emerging approach that has been increasingly applied in various fields, is able to reflect the unique chemical fingerprints of specific cellular processes in an organism. The assessment of such metabolite changes can be used to identify novel therapeutic biomarkers. In the present study, ¹H nuclear magnetic resonance (NMR) spectroscopy was used to analyze the extracellular metabolomic spectrum of the Tca8113 oral squamous carcinoma cell line, in which MDR was induced using the carboplatin (CBP) and pingyangmycin (PYM) chemotherapy drugs in vitro. The data were analyzed using the principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods. The results demonstrated that the extracellular metabolomic spectrum of metabolites such as glutamate, glycerophosphoethanol amine, α-Glucose and β-Glucose for the drug-induced Tca8113 cells was significantly different from the parental Tca8113 cell line. A number of biochemicals were also significantly different between the groups based on their NMR spectra, with drug-resistant cells presenting relatively higher levels of acetate and lower levels of lactate. In addition, a significantly higher peak was observed at δ 3.35 ppm in the spectrum of the PYM-induced Tca8113 cells. Therefore, ¹H NMR-based metabolomic analysis has a high potential for monitoring the formation of MDR during clinical tumor chemotherapy in the future.

KLK1 and ZG16B proteins and arginine-proline metabolism identified as novel targets to monitor atherosclerosis, acute coronary syndrome and recovery

We pursued here the identification of specific signatures of proteins and metabolites in urine which respond to atherosclerosis development, acute event and/or recovery. An animal model (rabbit) of atherosclerosis was developed and molecules responding to atherosclerosis silent development were identified. Those molecules were investigated in human urine from patients suffering an acute coronary syndrome (ACS), at onset and discharge. Kallikrein1 (KLK1) and zymogen granule protein16B (ZG16B) proteins, and l-alanine, l-arabitol, scyllo-inositol, 2-hydroxyphenilacetic acid, 3-hydroxybutyric acid and N-acetylneuraminic acid metabolites were found altered in response to atherosclerosis progression and the acute event, composing a molecular panel related to cardiovascular risk. KLK1 and ZG16B together with 3-hydroxybutyric acid, putrescine and 1-methylhydantoin responded at onset but also showed normalized levels at discharge, constituting a molecular panel to monitor recovery. The observed decreased of KLK1 is in alignment with the protective mechanism of the kallikrein-kinin system. The connection between KLK1 and ZG16B shown by pathway analysis explains reduced levels of toll-like receptor 2 described in atherosclerosis. Metabolomic analysis revealed arginine and proline metabolism, glutathione metabolism and degradation of ketone bodies as the three main pathways altered. In conclusion, two novel urinary panels of proteins and metabolites are here for the first time shown related to atherosclerosis, ACS and patient's recovery.

A type 2 biomarker separates relapsing-remitting from secondary progressive multiple sclerosis

OBJECTIVE

We tested whether it is possible to differentiate relapsing-remitting (RR) from secondary progressive (SP) disease stages in patients with multiple sclerosis (MS) using a combination of nuclear magnetic resonance (NMR) metabolomics and partial least squares discriminant analysis (PLS-DA) of biofluids, which makes no assumptions on the underlying mechanisms of disease.

METHODS

Serum samples were obtained from patients with primary progressive MS (PPMS), SPMS, and RRMS; patients with other neurodegenerative conditions; and age-matched controls. Samples were analyzed by NMR and PLS-DA models were derived to separate disease groups.

RESULTS

The PLS-DA models for serum samples from patients with MS enabled reliable differentiation between RRMS and SPMS. This approach also identified significant differences between the metabolite profiles of each of the MS groups (PP, SP, and RR) and the healthy controls, as well as predicting disease group membership with high specificity and sensitivity.

CONCLUSIONS

NMR metabolomics analysis of serum is a sensitive and robust method for differentiating between different stages of MS, yielding diagnostic markers without a priori knowledge of disease pathogenesis. Critically, this study identified and validated a type II biomarker for the RR to SP transition in patients with MS. This approach may be of considerable benefit in categorizing patients for treatment and as an outcome measure in future clinical trials.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that serum metabolite profiles accurately distinguish patients with different subtypes and stages of MS.

Serum metabolomic profiles evaluated after surgery may identify patients with oestrogen receptor negative early breast cancer at increased risk of disease recurrence. Results from a retrospective study

PURPOSE

Metabolomics is a global study of metabolites in biological samples. In this study we explored whether serum metabolomic spectra could distinguish between early and metastatic breast cancer patients and predict disease relapse.

METHODS

Serum samples were analysed from women with metastatic (n = 95) and predominantly oestrogen receptor (ER) negative early stage (n = 80) breast cancer using high resolution nuclear magnetic resonance spectroscopy. Multivariate statistics and a Random Forest classifier were used to create a prognostic model for disease relapse in early patients.

RESULTS

In the early breast cancer training set (n = 40), metabolomics correctly distinguished between early and metastatic disease in 83.7% of cases. A prognostic risk model predicted relapse with 90% sensitivity (95% CI 74.9-94.8%), 67% specificity (95% CI 63.0-73.4%) and 73% predictive accuracy (95% CI 70.6-74.8%). These results were reproduced in an independent early breast cancer set (n = 40), with 82% sensitivity, 72% specificity and 75% predictive accuracy. Disease relapse was associated with significantly lower levels of histidine (p = 0.0003) and higher levels of glucose (p = 0.01), and lipids (p = 0.0003), compared with patients with no relapse.

CONCLUSIONS

The performance of a serum metabolomic prognostic model for disease relapse in individuals with ER-negative early stage breast cancer is promising. A confirmation study is ongoing to better define the potential of metabolomics as a host and tumour-derived prognostic tool.

Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN

Data processing for 1D NMR spectra is a key bottleneck for metabolomic and other complex-mixture studies, particularly where quantitative data on individual metabolites are required. We present a protocol for automated metabolite deconvolution and quantification from complex NMR spectra by using the Bayesian automated metabolite analyzer for NMR (BATMAN) R package. BATMAN models resonances on the basis of a user-controllable set of templates, each of which specifies the chemical shifts, J-couplings and relative peak intensities for a single metabolite. Peaks are allowed to shift position slightly between spectra, and peak widths are allowed to vary by user-specified amounts. NMR signals not captured by the templates are modeled non-parametrically by using wavelets. The protocol covers setting up user template libraries, optimizing algorithmic input parameters, improving prior information on peak positions, quality control and evaluation of outputs. The outputs include relative concentration estimates for named metabolites together with associated Bayesian uncertainty estimates, as well as the fit of the remainder of the spectrum using wavelets. Graphical diagnostics allow the user to examine the quality of the fit for multiple spectra simultaneously. This approach offers a workflow to analyze large numbers of spectra and is expected to be useful in a wide range of metabolomics studies.

Systems biochemical responses of rats to Kansui and vinegar-processed Kansui exposure by integrated metabonomics

ETHNOPHARMACOLOGICAL RELEVANCE

The dried root of Kansui (Euphorbia kansui L.) is an effective and commonly used traditional Chinese medicine. Even so, Kansui cannot be satisfactorily applied clinically because of toxic side effects. In China, the most common Kansui-processing method uses vinegar to reduce its toxicity. The present study was designed to investigate the toxic effects caused by Kansui and evaluate detoxification of Kansui by vinegar processing of Kansui.

MATERIALS AND METHOD

Thirty male Sprague Dawley (SD) rats were randomly assigned to five groups of six rats. Two experimental groups were oral gavaged with 7.875 and 15.75 g Kansui/kg body weight, two treated with 7.875 and 15.75 g VP-Kansui/kg body weight for 14 d, and the control group concurrently subjected to oral gavage with only distilled water. On day 14, plasma, liver and kidney tissues were collected from all rats for biochemistry assessments, histopathological examination, and NMR analyses.

RESULTS

The metabonome of rats treated with Kansui and vinegar-processed (VP-) Kansui was found to differ from that of controls. In liver extracts, the variational metabolites included elevated concentrations of isoleucine, leucine, valine, glutamate, and phenylalanine, with decreased taurine, glucose, and glycogen. However, changes in lysine, methionine, choline, phosphorylcholine, and tyrosine were only observed in Kansui-treated rats. In kidney extracts, prominent changes included elevations in isoleucine, leucine, valine, methionine, creatine/creatinine, and phenylalanine as well as decreased glutamine. Only Kansui treatment induced variations in alanine, lysine, acetate, choline, and phosphorylcholine.

CONCLUSION

Perturbations in endogenous metabolites induced by Kansui correlated with disturbances in glycolysis and amino acid and lipid metabolism, while biochemical pathway disorders caused by VP-Kansui only involved glycolysis and amino acid metabolism. All results were confirmed by histopathological examination of liver and kidney tissues and clinical biochemistry analyses.

1H NMR-based metabolomics methods for chemical genomics experiments

Metabolomics and chemical genomics studies can each provide unique insights into plant biology. Although a variety of analytical techniques can be used for the interrogation of plant systems, nuclear magnetic resonance (NMR) provides unbiased characterization of abundant metabolites. An example methodology is provided for probing the metabolism of Arabidopsis thaliana in a chemical genomics experiment including methods for tissue treatment, tissue collection, metabolite extraction, and methods to minimize variance in biological and technical sample replicates. Additionally, considerations and methods for data analysis, including multivariate statistics, univariate statistics, and data interpretation are included. The process is illustrated by examining the metabolic effects of chemical treatment of Arabidopsis with Sortin 1, also known as vacuolar protein sorting inhibitor 1. Sortin 1 was applied to Arabidopsis seedlings to examine metabolic effects in a chemical genomics experiment and to demonstrate the utility of metabolomics in conjunction with other "omics" techniques.

A machine-learned predictor of colonic polyps based on urinary metabolomics

We report an automated diagnostic test that uses the NMR spectrum of a single spot urine sample to accurately distinguish patients who require a colonoscopy from those who do not. Moreover, our approach can be adjusted to tradeoff between sensitivity and specificity. We developed our system using a group of 988 patients (633 normal and 355 who required colonoscopy) who were all at average or above-average risk for developing colorectal cancer. We obtained a metabolic profile of each subject, based on the urine samples collected from these subjects, analyzed via ¹H-NMR and quantified using targeted profiling. Each subject then underwent a colonoscopy, the gold standard to determine whether he/she actually had an adenomatous polyp, a precursor to colorectal cancer. The metabolic profiles, colonoscopy outcomes, and medical histories were then analysed using machine learning to create a classifier that could predict whether a future patient requires a colonoscopy. Our empirical studies show that this classifier has a sensitivity of 64% and a specificity of 65% and, unlike the current fecal tests, allows the administrators of the test to adjust the tradeoff between the two.

Toward personalized nutrition: comprehensive phytoprofiling and metabotyping

Nutrition research is increasingly concerned with the complex interactions between multicomponent dietary ingredients and the human metabolic regulatory system. The substantiation of nutritional health benefits is challenged by the intrinsic complexity of macro- and micronutrients and individualized human metabolic responses. Metabonomics, uniquely suited to assess metabolic responses to deficiencies or excesses of nutrients, is used to characterize the metabolic phenotype of individuals integrating genetic polymorphisms, metabolic interactions with commensal and symbiotic partners such as gut microbiota, as well as environmental and behavioral factors including dietary preferences. The two profiling strategies, metabolic phenotyping (metabotyping) and phytochemical profiling (phytoprofiling), greatly facilitate the measurement of these important health determinants and the discovery of new biomarkers associated with nutritional requirements and specific phytochemical interventions. This paper presents an overview of the applications of these two profiling approaches for personalized nutrition research, with a focus on recent advances in the study of the role of phytochemicals in regulating the human or animal metabolic regulatory system.

Urine metabonomic profiling of a female adolescent with PIT-1 mutation before and during growth hormone therapy: insights into the metabolic effects of growth hormone

OBJECTIVE

Growth hormone (GH) is a protein hormone with important roles in growth and metabolism. The objective of this study was to investigate the metabolism of a human subject with severe GH deficiency (GHD) due to a PIT-1 gene mutation and the metabolic effects of GH therapy using Nuclear Magnetic Resonance (NMR)-based metabonomics. NMR-based metabonomics is a platform that allows the metabolic profile of biological fluids such as urine to be recorded, and any alterations in the profile modulated by GH can potentially be detected.

DESIGN

Urine samples were collected from a female subject with severe GHD before, during and after GH therapy, and from healthy age- and sex-matched controls and analysed with NMR-based metabonomics.

SETTING

The samples were collected at a hospital and the study was performed at a research facility.

PARTICIPANTS

We studied a 17 year old female adolescent with severe GHD secondary to PIT-1 gene mutation who had reached final adult height and who had ceased GH therapy for over 3 years. The subject was subsequently followed for 5 years with and without GH therapy. Twelve healthy age-matched female subjects acted as control subjects.

INTERVENTION

The GH-deficient subject re-commenced GH therapy at a dose of 1 mg/day to normalise serum IGF-1 levels.

MAIN OUTCOME MEASURES

Urine metabolic profiles were recorded using NMR spectroscopy and analysed with multivariate statistics to distinguish the profiles at different time points and identify significant metabolites affected by GH therapy.

RESULTS

NMR-based metabonomics revealed that the metabolic profile of the GH-deficient subject altered with GH therapy and that her profile was different from healthy controls before, and during withdrawal of GH therapy.

CONCLUSION

This study illustrates the potential use of NMR-based metabonomics for monitoring the effects of GH therapy on metabolism by profiling the urine of GH-deficient subjects. Further controlled studies in larger numbers of GH-deficient subjects are required to determine the clinical benefits of NMR-based metabonomics in subjects receiving GH therapy.

Metabolomics in lung inflammation:a high-resolution (1)h NMR study of mice exposedto silica dust

ABSTRACT Here we report the first (1)H NMR metabolomics studies on excised lungs and bronchoalveolar lavage fluid (BALF) from mice exposed to crystalline silica. High-resolution (1)H NMR metabolic profiling on intact excised lungs was performed using slow magic angle sample spinning (slow-MAS) (1)H PASS (phase-altered spinning sidebands) at a sample spinning rate of 80 Hz. Metabolic profiling on BALF was completed using fast magic angle spinning at 2 kHz. Major findings are that the relative concentrations of choline, phosphocholine (PC), and glycerophosphocholine (GPC) were statistically significantly increased in silica-exposed mice compared to sham controls, indicating an altered membrane choline phospholipids metabolism (MCPM). The relative concentrations of glycogen/glucose, lactate, and creatine were also statistically significantly increased in mice exposed to silica dust, suggesting that cellular energy pathways were affected by silica dust. Elevated levels of glycine, lysine, glutamate, proline, and 4-hydroxyproline were also increased in exposed mice, suggesting the activation of a collagen pathway. Furthermore, metabolic profiles in mice exposed to silica dust were found to be spatially heterogeneous, consistent with regional inflammation revealed by in vivo magnetic resonance imaging (MRI).

Metabonomic classification and detection of small molecule biomarkers of malignant pleural effusions

To date, most research has been focused on the benign molecules in pleural effusions, and diagnosis of malignant ones still remains challenging. In the present study, targeting the small molecules as potential biomarkers to predict the malignancy of the effusions, the metabolic profiles of 81 clinical pleural effusions (41 malignant effusions from lung cancer and 40 benign ones) were investigated through a NMR-based metabonomic approach. In (1)H NMR analysis, a total of ten small molecules in the effusions were simultaneously determined. Significantly higher mean values of valine, lactate, and alanine and markedly lower signal intensities of acetoacetate, trimethylamine-N-oxide, and α- and β-glucose were observed in malignant pleural effusions compared with those in benign ones. DFA modeling of NMR spectra subjected to a validation allowed the malignant effusions to be discriminated from benign ones in both training and validation groups. Currently, the conventional clinical analyses on chemical constituents in effusions could not provide a reliable prediction of malignancy of the effusions; the present results revealed that the small molecules might serve as useful biomarkers for diagnosis of the effusions, and the present NMR-based metabonomic approach provided a valuable potential to rapidly and sensitively predict the malignancy of the pleural effusions.

Annotation of the human adult urinary metabolome and metabolite identification using ultra high performance liquid chromatography coupled to a linear quadrupole ion trap-Orbitrap mass spectrometer

Metabolic profiles of biofluids obtained by atmospheric pressure ionization mass spectrometry-based technologies contain hundreds to thousands of features, most of them remaining unknown or at least not characterized in analytical systems. We report here on the annotation of the human adult urinary metabolome and metabolite identification from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics data sets. Features of biological interest were first of all annotated using the ESI-MS database of the laboratory. They were also grouped, thanks to software tools, and annotated using public databases. Metabolite identification was achieved using two complementary approaches: (i) formal identification by matching chromatographic retention times, mass spectra, and also product ion spectra (if required) of metabolites to be characterized in biological data sets to those of reference compounds and (ii) putative identification from biological data thanks to MS/MS experiments for metabolites not available in our chemical library. By these means, 384 metabolites corresponding to 1484 annotated features (659 in negative ion mode and 825 in positive ion mode) were characterized in human urine samples. Of these metabolites, 192 and 66 were formally and putatively identified, respectively, and 54 are reported in human urine for the first time. These lists of features could be used by other laboratories to annotate their ESI-MS metabolomics data sets.

Analysis of bacterial biofilms using NMR-based metabolomics

Infectious diseases can be difficult to cure, especially if the pathogen forms a biofilm. After decades of extensive research into the morphology, physiology and genomics of biofilm formation, attention has recently been directed toward the analysis of the cellular metabolome in order to understand the transformation of a planktonic cell to a biofilm. Metabolomics can play an invaluable role in enhancing our understanding of the underlying biological processes related to the structure, formation and antibiotic resistance of biofilms. A systematic view of metabolic pathways or processes responsible for regulating this 'social structure' of microorganisms may provide critical insights into biofilm-related drug resistance and lead to novel treatments. This review will discuss the development of NMR-based metabolomics as a technology to study medically relevant biofilms. Recent advancements from case studies reviewed in this manuscript have shown the potential of metabolomics to shed light on numerous biological problems related to biofilms.

1H NMR-based metabonomics study of the urinary biochemical changes in Kansui treated rat

ETHNOPHARMACOLOGICAL RELEVANCE

The dried root of Kansui (Euphorbia kansui L.) is a commonly used and effective traditional Chinese medicine (TCM).

AIM OF THE STUDY

We combined the urinary metabolites alteration and traditional assays of Kansui-induced rats to discuss the mechanism of toxicity of Kansui.

MATERIALS AND METHODS

The Sprague-Dawley rats were dosed with 7.875g Kansui/kg weight and 15.75g Kansui/kg weight. Urine samples were collected at day -1 (before treatment), and days 7, 14 and 21 for NMR analysis. Plasma and liver and kidney tissues were collected at day 14 for biochemical assays and histopathological examination, respectively.

RESULTS

The metabonome of rats treated with Kansui differed markedly from that of the controls. This was confirmed by the histopathology of liver and kidney tissue and clinical biochemistry analysis. The toxicity of Kansui accumulated with dosing time, and persisted even when treatment was stopped. The corresponding biochemical pathways alterations included inhibited TCA cycle, increased anaerobic glycolysis, and perturbed amino acids metabolism.

CONCLUSION

The biochemical pathways disorder conjunction with histopathology changes provides new clues to evaluate the toxicity of Kansui from a systematic and holistic view.

Investigating the effect of antioxidant treatment on the protective effect of preconditioning in anesthetized rabbits

Reactive oxygen and nitrogen species are critical in preconditioning (PC). We sought to determine the effect of N-2-mercaptopropionyl glycine (MPG) on infarct size and on the oxidative status. Rabbits were exposed to 30-minute regional ischemia of the heart, which was followed by 3-hour reperfusion: (1) a control group without further intervention, (2) a PC1 group that was subjected to one cycle of PC, (3) a PC4 group that was subjected to 4 cycles of PC, (4) an MPG group that was treated with MPG for 60 minutes, starting 10 minutes before reperfusion, (5) MPG-PC1, and (6) the MPG-PC4 groups that were treated with the same dose of MPG and with 1 or 4 cycles of PC, respectively. Blood samples were drawn and collected for metabonomic analysis. In another series of experiments, 6 groups respective to the described ones were subjected to 30-minute regional ischemia of the heart and 20 minutes of reperfusion, after which pieces of heart tissue were quickly excised for malondialdehyde, nitrotyrosine, and glutathione content assessment. All PC and MPG groups developed smaller infarct size compared with control (16.5% ± 3.9%, 13.7% ± 3.1%, 18.6% ± 5.0%, 9.7% ± 2.0%, 15.0% ± 2.8% vs. 48.05% ± 7.2%; P < 0.05). MPG did not prevent lipid peroxidation and nitrotyrosine formation but enhanced the glutathione content. PC and MPG induced similar nuclear magnetic resonance changes. Long MPG infusion reduces the infarct size without abolishing the effect of PC, providing novel insights into the activity of MPG in PC.

Prediction of skeletal muscle and fat mass in patients with advanced cancer using a metabolomic approach

Urine and plasma metabolites originate from endogenous metabolic pathways in different organs and exogenous sources (diet). Urine and plasma were obtained from advanced cancer patients and investigated to determine if variations in lean and fat mass, dietary intake, and energy metabolism relate to variation in metabolite profiles. Patients (n = 55) recorded their diets for 3 d and after an overnight fast they were evaluated by DXA and indirect calorimetry. Metabolites were measured by NMR and direct injection MS. Three algorithms were used [partial least squares discriminant-analysis, support vector machines (SVM), and least absolute shrinkage and selection operator] to relate patients' plasma/urine metabolic profile with their dietary/physiological assessments. Leave-one-out cross-validation and permutation testing were conducted to determine statistical validity. None of the algorithms, using 63 urine metabolites, could learn to predict variations in individual's resting energy expenditure, respiratory quotient, or their intake of total energy, fat, sugar, or carbohydrate. Urine metabolites predicted appendicular lean tissue (skeletal muscle) with excellent cross-validation accuracy (98% using SVM). Total lean tissue correlated highly with appendicular muscle (Pearson r = 0.98; P < 0.0001) and gave similar cross-validation accuracies. Fat mass was effectively predicted using the 63 urine metabolites or the 143 plasma metabolites, exclusively. In conclusion, in this population, lean and fat mass variation could be effectively predicted using urinary metabolites, suggesting a potential role for metabolomics in body composition research. Furthermore, variation in lean and fat mass potentially confounds metabolomic studies attempting to characterize diet or disease conditions. Future studies should account or correct for such variation.

A metabolomic approach for diagnosis of experimental sepsis

BACKGROUND

The search for reliable diagnostic biomarkers of sepsis remains necessary. Assessment of global metabolic profiling using quantitative nuclear magnetic resonance (NMR)-based metabolomics offers an attractive modern methodology for fast and comprehensive determination of multiple circulating metabolites and for defining the metabolic phenotype of sepsis.

OBJECTIVE

To develop a novel NMR-based metabolomic approach for diagnostic evaluation of sepsis.

METHODS

Male Sprague-Dawley rats (weight 325-375 g) underwent cecal ligation and puncture (n = 14, septic group) or sham procedure (n = 14, control group) and 24 h later were euthanized. Lung tissue, bronchoalveolar lavage (BAL) fluid, and serum samples were obtained for (1)H NMR and high-resolution magic-angle spinning analysis. Unsupervised principal components analysis was performed on the processed spectra, and a predictive model for diagnosis of sepsis was constructed using partial least-squares discriminant analysis.

RESULTS

NMR-based metabolic profiling discriminated characteristics between control and septic rats. Characteristic metabolites changed markedly in septic rats as compared with control rats: alanine, creatine, phosphoethanolamine, and myoinositol concentrations increased in lung tissue; creatine increased and myoinositol decreased in BAL fluid; and alanine, creatine, phosphoethanolamine, and acetoacetate increased whereas formate decreased in serum. A predictive model for diagnosis of sepsis using these metabolites classified cases with sensitivity and specificity of 100%.

CONCLUSIONS

NMR metabolomic analysis is a potentially useful technique for diagnosis of sepsis. The concentrations of metabolites involved in energy metabolism and in the inflammatory response change in this model of sepsis.

Biological responses to perfluorododecanoic acid exposure in rat kidneys as determined by integrated proteomic and metabonomic studies

Background

Perfluorododecanoic acid (PFDoA) is a perfluorinated carboxylic chemical (PFC) that has broad applications and distribution in the environment. While many studies have focused on hepatotoxicity, immunotoxicity, and reproductive toxicity of PFCAs, few have investigated renal toxicity.

Methodology/Principal Findings

Here, we used comparative proteomic and metabonomic technologies to provide a global perspective on renal response to PFDoA. Male rats were exposed to 0, 0.05, 0.2, and 0.5 mg/kg/day of PFDoA for 110 days. After 2-D DIGE and MALDI TOF/TOF analysis, 79 differentially expressed proteins between the control and the PFDoA treated rats (0.2 and 0.5 mg-dosed groups) were successfully identified. These proteins were mainly involved in amino acid metabolism, the tricarboxylic acid cycle, gluconeogenesis, glycolysis, electron transport, and stress response. Nuclear magnetic resonance-based metabonomic analysis showed an increase in pyruvate, lactate, acetate, choline, and a variety of amino acids in the highest dose group. Furthermore, the profiles of free amino acids in the PFDoA treated groups were investigated quantitatively by high-coverage quantitative iTRAQ-LC MS/MS, which showed levels of sarcosine, asparagine, histidine, 1-methylhistidine, Ile, Leu, Val, Trp, Tyr, Phe, Cys, and Met increased markedly in the 0.5 mg dosed group, while homocitrulline, α-aminoadipic acid, β-alanine, and cystathionine decreased.

Conclusion/Significance

These observations provide evidence that disorders in glucose and amino acid metabolism may contribute to PFDoA nephrotoxicity. Additionally, α_2u globulin may play an important role in protecting the kidneys from PFDoA toxicity.

Prediction of variability in CYP3A4 induction using a combined 1H NMR metabonomics and targeted UPLC-MS approach

The activity of Cytochrome P450 3A4 (CYP3A4) enzyme is associated with many adverse or poor therapeutic responses to drugs. We used (1)H NMR-based metabonomics to identify a metabolic signature associated with variation in induced CYP3A4 activity. A total of 301 female twins, aged 45--84, participated in this study. Each volunteer was administered a potent inducer of CYP3A4 (St. John's Wort) for 14 days and the activity of CYP3A4 was quantified through the metabolism of the exogenously administered probe drug quinine sulfate (300 mg). Pre- and postintervention fasting urine samples were used to obtain metabolite profiles, using (1)H NMR spectroscopy, and were analyzed using UPLC--MS to obtain a marker for CYP3A4 induction, via the ratio of 3-hydroxyquinine to quinine (3OH-Q:Q). Multiple linear regression was used to build a predictive model for 3OH-Q:Q values based on the preintervention metabolite profiles. A combination of seven metabolites and seven covariates showed a strong (r = 0.62) relationship with log(3OH-Q:Q). This regression model demonstrated significant (p < 0.00001) predictive ability when applied to an independent validation set. Our results highlight the promise of metabonomics for predicting CYP3A4-mediated drug response.