Metabolomic biomarkers in a model of asthma exacerbation: urine nuclear magnetic resonance

Metabolomics in Animal Models of Bronchial Asthma and Its Translational Importance for Clinics

Bronchial asthma is an extremely heterogenous chronic respiratory disorder with several distinct endotypes and phenotypes. These subtypes differ not only in the pathophysiological changes and/or clinical features but also in their response to the treatment. Therefore, precise diagnostics represent a fundamental condition for effective therapy. In the diagnostic process, metabolomic approaches have been increasingly used, providing detailed information on the metabolic alterations associated with human asthma. Further information is brought by metabolomic analysis of samples obtained from animal models. This article summarizes the current knowledge on metabolomic changes in human and animal studies of asthma and reveals that alterations in lipid metabolism, amino acid metabolism, purine metabolism, glycolysis and the tricarboxylic acid cycle found in the animal studies resemble, to a large extent, the changes found in human patients with asthma. The findings indicate that, despite the limitations of animal modeling in asthma, pre-clinical testing and metabolomic analysis of animal samples may, together with metabolomic analysis of human samples, contribute to a novel way of personalized treatment of asthma patients.

Sex differences in plasma metabolites in a guinea pig model of allergic asthma

Sex seems to be a contributing factor in the pathogenesis of bronchial asthma. This study aimed to find sex-related differences in metabolome measured by hydrogen-1 nuclear magnetic resonance ((1)H NMR) spectroscopy in healthy and ovalbumin (OVA)-sensitized guinea pigs. Adult male and female animals were divided into controls and OVA-sensitized groups. OVA-sensitization was performed by OVA systemic and inhalational administration within 14 days; on day 15, animals were killed by anesthetic overdose followed by exsanguination. Blood was taken and differential white blood cell count was measured. Left lung was saline-lavaged and differential cell count in the bronchoalveolar lavage fluid (BALF) was measured. After blood centrifugation, plasma was processed for (1)H NMR analysis. Metabolomic data was evaluated by principal component analysis (PCA). Eosinophil counts elevated in the BALF confirming eosinophil-mediated inflammation in OVA-sensitized animals of both sexes. Sex differences for lactate, glucose, and citrate were found in controls, where these parameters were lower in males than in females. In OVA-sensitized males higher glucose and lower pyruvate were found compared to controls. OVA-sensitized females showed lower lactate, glucose, alanine, 3-hydroxy-butyrate, creatine, pyruvate, and succinate concentrations compared to controls. In OVA-sensitized animals, lactate concentration was lower in males. Data from females (healthy and OVA-sensitized) were generally more heterogeneous. Significant sex differences in plasma concentrations of metabolites were found in both healthy and OVA-sensitized animals suggesting that sex may influence the metabolism and may thereby contribute to different clinical picture of asthma in males and females.

Investigating the Underlying Mechanisms of Ardisia japonica Extract's Anti-Blood-Stasis Effect via Metabolomics and Network Pharmacology

OBJECTIVE

Our study aims to assess Ardisia japonica (AJ)'s anti-blood-stasis effect and its underlying action mechanisms.

METHODS

The primary components of AJ were determined using liquid chromatography-mass spectrometry (LC-MS). The blood stasis model was used to investigate the anti-blood-stasis effect of AJ extract. The underlying mechanisms of AJ against blood stasis were investigated via network pharmacology, molecular docking, and plasma non-targeted metabolomics.

RESULTS

In total, 94 compounds were identified from an aqueous extract of AJ, including terpenoids, phenylpropanoids, alkaloids, and fatty acyl compounds. In rats with blood stasis, AJ reduced the area of stasis, decreased the inflammatory reaction in the liver and lungs of rats, lowered the plasma viscosity, increased the index of erythrocyte deformability, and decreased the index of erythrocyte aggregation, suggesting that AJ has an anti-blood-stasis effect. Different metabolites were identified via plasma untargeted metabolomics, and it was found that AJ exerts its anti-blood-stasis effect by reducing inflammatory responses through the cysteine and methionine metabolism, linolenic acid metabolism, and sphingolipid metabolism. For the effect of AJ on blood stasis syndrome, the main active ingredients predicted via network pharmacology include sinensetin, galanin, isorhamnetin, kaempferol, wogonin, quercetin, and bergenin, and their targets were TP53, HSP90AA1, VEGFA, AKT1, EGFR, and PIK3CA that were mainly enriched in the PI3K/AKT and MAPK signaling pathways, which modulate the inflammatory response. Molecular docking was also performed, and the binding energies of these seven compounds to six proteins were less than -5, indicating that the chemical components bind to the target proteins.

CONCLUSIONS

This study suggests AJ effectively prevents blood stasis by reducing inflammation.

Itaconate Suppresses the Activation of Mitochondrial NLRP3 Inflammasome and Oxidative Stress in Allergic Airway Inflammation

Itaconate has emerged as a novel anti-inflammatory and antioxidative endogenous metabolite, yet its role in allergic airway inflammation (AAI) and the underlying mechanism remains elusive. Here, the itaconate level in the lung was assessed by High Performance Liquid Chromatography (HPLC), and the effects of the Irg1/itaconate pathway on AAI and alveolar macrophage (AM) immune responses were evaluated using an ovalbumin (OVA)-induced AAI model established by wild type (WT) and Irg1-/- mice, while the mechanism of this process was investigated by metabolomics analysis, mitochondrial/cytosolic protein fractionation and transmission electron microscopy in the lung tissues. The results demonstrated that the Irg1 mRNA/protein expression and itaconate production in the lung were significantly induced by OVA. Itaconate ameliorated while Irg1 deficiency augmented AAI, and this may be attributed to the fact that itaconate suppressed mitochondrial events such as NLRP3 inflammasome activation, oxidative stress and metabolic dysfunction. Furthermore, we identified that the Irg1/itaconate pathway impacted the NLRP3 inflammasome activation and oxidative stress in AMs. Collectively, our findings provide evidence for the first time, supporting the conclusion that in the allergic lung, the itaconate level is markedly increased, which directly regulates AMs' immune responses. We therefore propose that the Irg1/itaconate pathway in AMs is a potential anti-inflammatory and anti-oxidative therapeutic target for AAI.

Biomarkers of Mycoplasma pneumoniae pneumonia in children by urine metabolomics based on Q Exactive liquid chromatography/tandem mass spectrometry

RATIONALE

Mycoplasma pneumoniae has become one of the common pathogens causing pediatric respiratory infections. In clinical diagnosis, throat swabs are very difficult to obtain from children, and there is a possibility of false positive results; hence, there are few clinically available diagnostic methods.

METHODS

In this study, Q Exactive liquid chromatography/tandem mass spectrometry was used to analyze the metabolites in the urine of healthy children (HC) and M. pneumoniae pneumonia in children (MPPC) patients. A multivariate statistical analysis was performed to screen the differential metabolites. Based on the HMDB and KEGG, the possible metabolic pathways subject to biological alteration were identified.

RESULTS

Compared with HC, 73 different metabolites in MPPC patients disrupted nine metabolic pathways through different change trends; after integrating various parameters, 20 significantly different metabolites were identified as MPPC potential biomarkers. Through the above two analysis modes, acetylphosphate and 2,5-dioxopentanoate were both screened out and used as potential biomarkers for the early diagnosis of MPPC for the first time.

CONCLUSIONS

The characterization of 20 potential biomarkers provides a scientific basis for predicting and diagnosing MPPC. This article further indicates that urine metabolic profiling has great potential in diagnosing MPPC and can effectively prevent the disease from causing further deterioration.

Metabolomic profiling of human pluripotent stem cell differentiation into lung progenitors

Metabolism is vital to cellular function and tissue homeostasis during human lung development. In utero, embryonic pluripotent stem cells undergo endodermal differentiation toward a lung progenitor cell fate that can be mimicked in vitro using induced human pluripotent stem cells (hiPSCs) to study genetic mutations. To identify differences between wild-type and surfactant protein B (SFTPB)-deficient cell lines during endoderm specification toward lung, we used an untargeted metabolomics approach to evaluate the developmental changes in metabolites. We found that the metabolites most enriched during the differentiation from pluripotent stem cell to lung progenitor cell, regardless of cell line, were sphingomyelins and phosphatidylcholines, two important lipid classes in lung development. The SFTPB mutation had no metabolic impact on early endodermal lung development. The identified metabolite signatures during lung progenitor cell differentiation may be utilized as biomarkers for normal embryonic lung development.

Metabolomics analysis of bronchoalveolar lavage fluid samples in horses with naturally-occurring asthma and experimentally-induced airway inflammation

The present work characterized the metabolomic profile of bronchoalveolar lavage fluid (BALF) in healthy horses, experimentally-induced airway inflammation by lipopolysaccharide (LPS) nebulization, and naturally-occurring asthma (n = 3 in each group). All animals underwent clinical and upper airway endoscopic examinations, and bronchoalveolar lavage. BALF supernatant samples were subjected to metabolic analysis based on gas chromatography-mass spectrometry (GC-MS). Overall, 67 peaks were obtained from BALF GC-MS analysis, corresponding to 53 metabolites which were categorized according to chemical class, such as organic acids, fatty acids, nucleosides or their derivatives, amino acids, peptides or their derivatives, carbohydrates, and other compounds. Our results showed that the airway inflammation induction model with LPS produced the same pattern of metabolite changes as in horses with naturally occurring asthma. Metabolic pathway analysis was done by means of Fisher's exact test, for detection of metabolites over-represented in asthma affected-horses and LPS-induced airway inflammation as compared with healthy horses. The most significant altered metabolic pathways were fatty acid biosynthesis, galactose metabolism and citrate cycle. These results suggest that the airway inflammation induction model with LPS is a good study model for asthma-affected horses, due to the similarity of the profile of inflammatory cells (specifically neutrophils) and similar metabolic alterations found in BALF that occur during the inflammatory process of the airways. Further research may increase understanding of metabolomics disturbances and their significance in the pathogenesis of equine asthma.

The Study of Dried Ginger and Linggan Wuwei Jiangxin Decoction Treatment of Cold Asthma Rats Using GC-MS Based Metabolomics

Dried ginger is the monarch drug in Linggan Wuwei Jiangxin (LGWWJX) decoction, which is used to treat cold asthma. The purpose of this study was to investigate and compare the effects of dried ginger and LGWWJX decoction for treatment of cold asthma rats at the metabolomics level using gas chromatography–mass spectrometry (GC–MS). OVA and ice water-induced cold asthma were induced in SD rats. The effects of dried ginger and LGWWJX decoction were evaluated by general morphological observation, hematoxylin and eosin staining, inflammatory cell count, IgE, IL-4, IFN-γ quantitation, and visceral index. GC-MS-based metabolomics was performed and analyzed using multivariate statistical analysis. Biomarker identification, pathway analysis, correlations between identified biomarker, and efficacy indices were performed. The results showed that dried ginger and LGWWJX decoction had obvious effects on cold asthma rats. Thirty-seven metabolites (15 in serum and 22 in urine) associated with cold asthma were identified. These metabolites were mainly carbohydrates, fatty acids and their products, organic acids, and others. Seven pathways were identified by MetaboAnalyst 4.0 metabolic pathway analysis. After intervention with dried ginger and LGWWJX decoction, the majority of altered metabolites and metabolic pathways returned to control levels. LGWWJX decoction regulated more metabolites of carbohydrates and fatty acids, which contribute to energy metabolism and oxidative stress in cold asthma, than dried ginger. We concluded that dried ginger and LGWWJX decoction both were effective for treatment of cold asthma. LGWWJX decoction was more effective than dried ginger for treatment of cold asthma. This study evaluated the effects of dried ginger and LGWWJX decoction on cold asthma at the metabolomics level. It provides a reference for the research on the compatibility of Chinese Medicine.

Quantitative determination of potential urine biomarkers of respiratory illnesses using new targeted metabolomic approach

The diagnosis of asthma and chronic obstructive pulmonary disease (COPD) can be challenging due to the overlap in their clinical presentations in some patients. There is a need for a more objective clinical test that can be routinely used in primary care settings. Through an untargeted ¹H NMR urine metabolomic approach, we identified a set of endogenous metabolites as potential biomarkers for the differentiation of asthma and COPD. A subset of these potential biomarkers contains 7 highly polar metabolites of diverse physicochemical properties. To the best of our knowledge, there is no liquid chromatography-tandem mass spectrometry (LC-MS/MS) method that evaluated more than two of the target metabolites in a single analytical run. The target metabolites belong to the families of monosaccharides, organic acids, amino acids, quaternary ammonium compounds and nucleic acids, rendering hydrophilic interaction liquid chromatography (HILIC) an ideal technology for their quantification. Since a clinical decision is to be made from patients data, a fully validated analytical method is required for biomarker validation. Method validation for endogenous metabolites is a daunting task since current guidelines were designed for exogenous compounds. As such, innovative approaches were adopted to meet the validation requirements. Herein, we describe a sensitive HILIC-MS/MS method for the quantification of the 7 endogenous urinary metabolites. Detection was achieved in the multiple reaction monitoring (MRM) mode with polarity switching, using quadrupole-linear ion trap instrument (QTRAP 6500) as well as single ion monitoring in the negative-ion mode. The method was fully validated according to the regulatory guidelines. Linearity was established between 6 and 21000 ng/mL and quality control samples demonstrated acceptable intra- and inter-day accuracy (85.7%-112%), intra- and inter-day precision (CV% <11.5%) as well as stability under various storage and sample processing conditions. To illustrate the method's applicability, the validated method was applied to the analysis of a small set of urine samples collected from asthma and COPD patients. Preliminary modelling of separation was generated using partial least square discriminant analysis (R² 0.752 and Q² 0.57). The adequate separation between patient samples confirms the diagnostic potential of these target metabolites as a proof-of-concept for the differentiation between asthma and COPD. However, more patient urine samples are needed in order to increase the statistical power of the analytical model.

Comparative analysis of creatinine and osmolality as urine normalization strategies in targeted metabolomics for the differential diagnosis of asthma and COPD

INTRODUCTION

Urine is an ideal matrix for metabolomics investigation due to its non-invasive nature of collection and its rich metabolite content. Despite the advancements in mass spectrometry and ¹H-NMR platforms in urine metabolomics, the statistical analysis of the generated data is challenged with the need to adjust for the hydration status of the person. Normalization to creatinine or osmolality values are the most adopted strategies, however, each technique has its challenges that can hinder its wider application. We have been developing targeted urine metabolomic methods to differentiate two important respiratory diseases, namely asthma and chronic obstructive pulmonary disease (COPD).

OBJECTIVE

To assess whether the statistical model of separation of diseases using targeted metabolomic data would be improved by normalization to osmolality instead of creatinine.

METHODS

The concentration of 32 metabolites was previously measured by two liquid chromatography-tandem mass spectrometry methods in 51 human urine samples with either asthma (n = 25) or COPD (n = 26). The data was normalized to creatinine or osmolality. Statistical analysis of the normalized values in each disease was performed using partial least square discriminant analysis (PLS-DA). Models of separation of diseases were compared.

RESULTS

We found that normalization to creatinine or osmolality did not significantly change the PLS-DA models of separation (R²Q² = 0.919, 0.705 vs R²Q² = 0.929, 0.671, respectively). The metabolites of importance in the models remained similar for both normalization methods.

CONCLUSION

Our findings suggest that targeted urine metabolomic data can be normalized for hydration using creatinine or osmolality with no significant impact on the diagnostic accuracy of the model.

Metabolic profiling of asthma in mice and the interventional effects of SPA using liquid chromatography and Q-TOF mass spectrometry

Asthma is a chronic inflammatory lung disease that leads to 250 000 deaths annually. There is a need to better understand asthma by identifying new pathogenic molecules. We conducted a liquid-chromatography time-of-flight mass spectrometry (LC-Q-TOF-MS)-based metabolomics study to test for asthma and investigate the interventional mechanisms of surfactant protein A (SPA) in OVA-induced asthma mice. The results revealed that asthma disturbed 32 metabolites in 9 metabolic pathways. After SPA treatment, the metabolomics profile found in asthma was significantly reversed, shifting much closer to that of the control group, indicating that SPA has therapeutic effects against asthma. Metabolomic pathway analysis by the ingenuity pathway analysis demonstrated that several pathways including fatty acid metabolism, lipid metabolism, and purine metabolism were significantly altered in asthma. This study offers new methodologies for the understanding of asthma and the mechanisms of SPA in treating asthma.

Allergic asthma: an overview of metabolomic strategies leading to the identification of biomarkers in the field

Allergic asthma is a prominent disease especially during childhood. Indoor allergens, in general, and particularly house dust mites (HDM) are the most prevalent sensitizers associated with allergic asthma. Available data show that 65-130 million people are mite-sensitized world-wide and as many as 50% of these are asthmatic. In fact, sensitization to HDM in the first years of life can produce devastating effects on pulmonary function leading to asthmatic syndromes that can be fatal. To date, there has been considerable research into the pathological pathways and structural changes associated with allergic asthma. However, limitations related to the disease heterogeneity and a lack of knowledge into its pathophysiology have impeded the generation of valuable data needed to appropriately phenotype patients and, subsequently, treat this disease. Here, we report a systematic and integral analysis of the disease, from airway remodelling to the immune response taking place throughout the disease stages. We present an overview of metabolomics, the management of complex multifactorial diseases through the analysis of all possible metabolites in a biological sample, obtaining a global interpretation of biological systems. Special interest is placed on the challenges to obtain biological samples and the methodological aspects to acquire relevant information, focusing on the identification of novel biomarkers associated with specific phenotypes of allergic asthma. We also present an overview of the metabolites cited in the literature, which have been related to inflammation and immune response in asthma and other allergy-related diseases.

Metabolomic study for monitoring of biomarkers in mouse plasma with asthma by gas chromatography-mass spectrometry

Asthma is a multifaceted chronic disease caused by an alteration of various genetic and environmental factors that is increasing in incidence worldwide. However, the biochemical mechanisms regarding asthma are not completely understood. Thus, we performed of metabolomic study for understanding of the biochemical events by monitoring of altered metabolism and biomarkers in asthma. In mice plasma, 27 amino acids(AAs), 24 fatty acids(FAs) and 17 organic acids(OAs) were determined by ethoxycarbonyl(EOC)/methoxime(MO)/tert-butyldimethylsilyl(TBDMS) derivatives with GC-MS. Their percentage composition normalized to the corresponding mean levels of control group. They then plotted as star symbol patterns for visual monitoring of altered metabolism, which were characteristic and readily distinguishable in control and asthma groups. The Mann-Whitney test revealed 25 metabolites, including eight AAs, nine FAs and eight OAs, which were significantly different (p<0.05), and orthogonal partial least-squares-discriminant analysis revealed a clear separation of the two groups. In classification analysis, palmitic acid and methionine were the main metabolites for discrimination between asthma and the control followed by pipecolic, lactic, α-ketoglutaric, and linoleic acids for high classification accuracy as potential biomarkers. These explain the metabolic disturbance in asthma for AAs and FAs including intermediate OAs related to the energy metabolism in the TCA cycle.

Development of a validated LC- MS/MS method for the quantification of 19 endogenous asthma/COPD potential urinary biomarkers

Obstructive airways inflammatory diseases sometimes show overlapping symptoms that hinder their early and correct diagnosis. Current clinical tests are tedious and are of inadequate specificity in special population such as the elderly and children. Therefore, we are developing tandem mass spectrometric (MS/MS) methods for targeted analysis of urine biomarkers. Recently, proton-nuclear magnetic resonance (¹H-NMR) analysis proposed 50 urinary metabolites as potential diagnostic biomarkers among asthma and chronic obstructive pulmonary disease (COPD) patients. Metabolites are divided into 3 groups based on chemical nature. For group 1 (amines and phenols, 19 urinary metabolites), we developed and validated a high performance liquid chromatographic (HPLC)-MS/MS method using differential isotope labeling (DIL) with dansyl chloride. Method development included the optimization of the derivatization reaction, the MS/MS conditions, and the chromatographic separation. Linearity varied from 2 to 4800 ng/mL and the use of ¹³C₂-labeled derivatives allowed for the correction of matrix effects as well as the unambiguous confirmation of the identity of each metabolite in the presence of interfering isomers in urine. Despite the challenges associated with method validation, the method was fully validated as per the food and drug administration (FDA) and the European medicines agency (EMA) recommendations. Validation criteria included linearity, precision, accuracy, dilution integrity, selectivity, carryover, and stability. Challenges in selectivity experiments included the isotopic contributions of the analyte towards its internal standard (IS), that was addressed via the optimization of the IS concentration. In addition, incurred sample analysis was performed to ensure that results from patient samples are accurate and reliable. The method was robust and reproducible and is currently being applied in a cohort of asthma and COPD patient urine samples for biomarker discovery purposes.

Mass spectrometric based approaches in urine metabolomics and biomarker discovery

Urine metabolomics has recently emerged as a prominent field for the discovery of non-invasive biomarkers that can detect subtle metabolic discrepancies in response to a specific disease or therapeutic intervention. Urine, compared to other biofluids, is characterized by its ease of collection, richness in metabolites and its ability to reflect imbalances of all biochemical pathways within the body. Following urine collection for metabolomic analysis, samples must be immediately frozen to quench any biogenic and/or non-biogenic chemical reactions. According to the aim of the experiment; sample preparation can vary from simple procedures such as filtration to more specific extraction protocols such as liquid-liquid extraction. Due to the lack of comprehensive studies on urine metabolome stability, higher storage temperatures (i.e. 4°C) and repetitive freeze-thaw cycles should be avoided. To date, among all analytical techniques, mass spectrometry (MS) provides the best sensitivity, selectivity and identification capabilities to analyze the majority of the metabolite composition in the urine. Combined with the qualitative and quantitative capabilities of MS, and due to the continuous improvements in its related technologies (i.e. ultra high-performance liquid chromatography [UPLC] and hydrophilic interaction liquid chromatography [HILIC]), liquid chromatography (LC)-MS is unequivocally the most utilized and the most informative analytical tool employed in urine metabolomics. Furthermore, differential isotope tagging techniques has provided a solution to ion suppression from urine matrix thus allowing for quantitative analysis. In addition to LC-MS, other MS-based technologies have been utilized in urine metabolomics. These include direct injection (infusion)-MS, capillary electrophoresis-MS and gas chromatography-MS. In this article, the current progresses of different MS-based techniques in exploring the urine metabolome as well as the recent findings in providing potentially diagnostic urinary biomarkers are discussed. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:115-134, 2017.

High-resolution metabolomics to identify urine biomarkers in corticosteroid-resistant asthmatic children

BACKGROUND

Corticosteroid (CS) treatment has been established as the first anti-inflammatory treatment for adults and children with asthma. However, a subset of patients fails to respond to combined systemic and inhaled CS treatment.

OBJECTIVE

This study was aimed at further understanding CS resistance among children with severe asthma.

METHODS

High-resolution metabolomics was performed on urine samples from CS-respondent (n = 15) and CS-nonrespondent (n = 15) children to determine possible urine biomarkers related to CS resistance. The metabolic phenotypes of CS responders and CS nonresponders were analyzed using bioinformatics including Manhattan plot with false- discovery rate, hierarchical cluster analysis, Kyoto Encyclopedia Genes and Genomes, and Mummichog pathway analysis.

RESULTS

The 2-way hierarchical cluster analysis study determined 30 metabolites showing significantly different levels between CS responders and CS nonresponders. The important metabolites annotated were 3,6-dihydronicotinic acid (126.05 m/z, RT: 106, [M+H]⁺), 3-methoxy-4-hydroxyphenyl(ethylene)glycol (185.05 m/z, RT: 155, [M+H]⁺), 3,4-dihydroxy-phenylalanine (198.07 m/z, RT: 446, [M+H]⁺), γ-glutamylcysteine (236.06 m/z, RT: 528, [M+S(34)+H]⁺), Cys-Gly, (253.06 m/z, RT: 528, [M-NH₃+H]⁺), and reduced Flavin mononucleotide (517.0794 m/z, RT: 533, [M+NaCl]⁺). Tyrosine metabolism, degradation of aromatic compounds, and glutathione metabolism are suggested to be significant pathways relating to CS resistance.

CONCLUSIONS

High-resolution metabolomics is a promising approach in asthma research. Five candidate markers were identified to be related to CS-resistant children with severe asthma. These compounds, upon validation, may contribute further in the understanding of CS resistance among children with severe asthma through the use of urine.

Urine metabolomic profiling of children with respiratory tract infections in the emergency department: a pilot study

BACKGROUND

Clinicians lack objective tests to help determine the severity of bronchiolitis or to distinguish a viral from bacterial causes of respiratory distress. We hypothesized that children with respiratory syncytial virus (RSV) infection would have a different metabolomic profile compared to those with bacterial infection or healthy controls, and this might also vary with bronchiolitis severity.

METHODS

Clinical information and urine-based metabolomic data were collected from healthy age-matched children (n = 37) and those admitted to hospital with a proven infection (RSV n = 55; Non-RSV viral n = 16; bacterial n = 24). Nuclear magnetic resonance (NMR) measured 86 metabolites per urine sample. Partial least squares discriminant analysis (PLS-DA) was performed to create models of separation.

RESULTS

Using a combination of metabolites, a strong PLS-DA model (R2 = 0.86, Q2 = 0.76) was created differentiating healthy children from those with RSV infection. This model had over 90 % accuracy in classifying blinded infants with similar illness severity. Two other models differentiated length of hospitalization and viral versus bacterial infection.

CONCLUSION

While the sample sizes remain small, this is the first report suggesting that metabolomic analysis of urine samples has the potential to become a diagnostic aid. Future studies with larger sample sizes are required to validate the utility of metabolomics in pediatric patients with respiratory distress.

Early infancy microbial and metabolic alterations affect risk of childhood asthma

Asthma is the most prevalent pediatric chronic disease and affects more than 300 million people worldwide. Recent evidence in mice has identified a "critical window" early in life where gut microbial changes (dysbiosis) are most influential in experimental asthma. However, current research has yet to establish whether these changes precede or are involved in human asthma. We compared the gut microbiota of 319 subjects enrolled in the Canadian Healthy Infant Longitudinal Development (CHILD) Study, and show that infants at risk of asthma exhibited transient gut microbial dysbiosis during the first 100 days of life. The relative abundance of the bacterial genera Lachnospira, Veillonella, Faecalibacterium, and Rothia was significantly decreased in children at risk of asthma. This reduction in bacterial taxa was accompanied by reduced levels of fecal acetate and dysregulation of enterohepatic metabolites. Inoculation of germ-free mice with these four bacterial taxa ameliorated airway inflammation in their adult progeny, demonstrating a causal role of these bacterial taxa in averting asthma development. These results enhance the potential for future microbe-based diagnostics and therapies, potentially in the form of probiotics, to prevent the development of asthma and other related allergic diseases in children.

Comparative effects of dexamethasone and bergenin on chronic bronchitis and their anti-inflammatory mechanisms based on NMR metabolomics

NMR metabolomics was applied to study the anti-inflammation mechanism of dexamethasone and bergenin on chronic bronchitis.

NMR-Based Metabolic Snapshot from Minibronchoalveolar Lavage Fluid: An Approach To Unfold Human Respiratory Metabolomics

The utility of mini bronchoalveolar lavage (mBAL) and its applicability in metabolomics has not been explored in the field of human respiratory disease. mBAL, "an archetype" of the local lung environment, ensures a potent technique to get the snapshot of the epithelial lining fluid afflicted to human lung disorders. Characterization of the mBAL fluid has potential to help in elucidating the composition of the alveoli and airways in the diseased state, yielding diagnostic information on clinical applicability. In this study, one of the first attempts has been made to comprehensively assign and detect metabolites in mBAL fluid, extracted from human lungs, by the composite use of 800 MHz 1D and 2D NMR, J-resolved homonuclear spectroscopy, COSY, TOCSY, and heteronuclear HSQC correlation methods. A foremost all-inclusive sketch of the 50 metabolites has been corroborated and assigned, which can be a resourceful archive to further lung-directed metabolomics, prognosis, and diagnosis. Thus, NMR-based mBALF studies, as proposed in this article, will leverage many more prospective respiratory researches for routine clinical application and prove to be a viable approach to mirror the key predisposing factors contributing to the onset of lung disease.

Metabolomic profiling of asthma and chronic obstructive pulmonary disease: A pilot study differentiating diseases

BACKGROUND

Differentiating asthma from other causes of chronic airflow limitation, such as chronic obstructive pulmonary disease (COPD), can be difficult in a typical outpatient setting. The inflammation of asthma typically is different than that of COPD, and the degree of inflammation and cellular damage varies with asthma severity. Metabolomics is the study of molecules created by cellular metabolic pathways.

OBJECTIVES

We hypothesized that the metabolic activity of adults with asthma would differ from that of adults with COPD. Furthermore, we hypothesized that nuclear magnetic resonance spectroscopy (NMR) would measure such differences in urine samples.

METHODS

Clinical and urine-based NMR data were collected on adults meeting the criteria of asthma and COPD before and after an exacerbation (n = 133 and 38, respectively) and from patients with stable asthma or COPD (n = 54 and 23, respectively). Partial least-squares discriminant analysis was performed on the NMR data to create models of separation (86 metabolites were measured per urine sample). Some subjects' metabolomic data were withheld from modeling to be run blindly to determine diagnostic accuracy.

RESULTS

Partial least-squares discriminant analysis of the urine NMR data found unique differences in select metabolites between patients with asthma and those with COPD seen in the emergency department and even in follow-up after exacerbation. By using these select metabolomic profiles, the model could correctly diagnose blinded asthma and COPD with greater than 90% accuracy.

CONCLUSION

This is the first report showing that metabolomic analysis of human urine samples could become a useful clinical tool to differentiate asthma from COPD.

Metabolomics study on serum of allergic bronchial asthma rabbits treated by Recuperating Lung decoction

This study found seven biological markers in the Recuperating Lung decoction, which intervenes in the mechanism of asthma.

The metabolomics of airway diseases, including COPD, asthma and cystic fibrosis

Chronic obstructive pulmonary disease (COPD), asthma and cystic fibrosis (CF) are characterized by airway obstruction and an inflammatory process. Reaching early diagnosis and discrimination of subtypes of these respiratory diseases are quite a challenging task than other chronic illnesses. Metabolomics is the study of metabolic pathways and the measurement of unique biochemical molecules generated in a living system. In the last decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. In this article, we review the current state of the metabolomics of COPD, asthma and CF with a focus on the different methods and instrumentation being used for the discovery of biomarkers in research and translation into clinic as diagnostic aids for the choice of patient-specific therapies.

Metabolomics Reveals Inflammatory-Linked Pulmonary Metabolic Alterations in a Murine Model of House Dust Mite-Induced Allergic Asthma

Although the house dust mite (HDM) is a major environmental aeroallergen that promotes the pathogenesis and severity of allergic asthma, it remains elusive if HDM exposures can induce global metabolism aberrations during allergic airway inflammation. Using an integrated gas and liquid chromatography mass spectrometry-based metabolomics and multiplex cytokine profile analysis, metabolic alterations and cytokine changes were investigated in the bronchoalveolar lavage fluid (BALF), serum, and lung tissues in experimental HDM-induced allergic asthma. Allergic pulmonary HDM exposures lead to pronounced eosinophilia, neutrophilia, and increases in inflammatory cytokines. Metabolomics analysis of the BALF, serum, and lung tissues revealed distinctive compartmental metabolic signatures, which included depleted carbohydrates, increased energy metabolites, and consistent losses of sterols and phosphatidylcholines. Pearson correlation analysis uncovered strong associations between specific metabolic alterations and inflammatory cells and cytokines, linking altered pulmonary metabolism to allergic airway inflammation. The clinically prescribed glucocorticoid prednisolone could modulate airway inflammation but was ineffective against the reversal of many HDM-induced metabolic alterations. Collectively, metabolomics reveal comprehensive pulmonary metabolic signatures in HDM-induced allergic asthma, with specific alterations in carbohydrates, lipids, sterols, and energy metabolic pathways. Altered pulmonary metabolism may be a major underlying molecular feature involved during HDM-induced allergic airway inflammation, linked to inflammatory cells and cytokines changes.

Metabolomics approach in allergic and rheumatic diseases

Metabolomics is the analysis of the concentration profiles of low molecular weight compounds present in biological fluids. Metabolites are nonpeptide molecules representing the end products of cellular activity. Therefore, changes in metabolite concentrations reveal the range of biochemical effects induced by a disease or its therapeutic intervention. Metabolomics has recently become feasible with the accessibility of new technologies, including mass spectrometry and high-resolution proton nuclear magnetic resonance, and has already been applied to several disorders. Indeed, it has the advantage of being a nontargeted approach for identifying potential biomarkers, which means that it does not require a preliminary knowledge of the substances to be studied. In this review, we summarize the main studies in which metabolomic approach was used in some allergic (asthma, atopic dermatitis) and rheumatic diseases (rheumatoid arthritis, systemic lupus erythematosus) to explore the feasibility of this technique as a novel diagnostic tool in these complex disorders.

Trials and tribulations of 'omics data analysis: assessing quality of SIMCA-based multivariate models using examples from pulmonary medicine

Respiratory diseases are multifactorial heterogeneous diseases that have proved recalcitrant to understanding using focused molecular techniques. This trend has led to the rise of 'omics approaches (e.g., transcriptomics, proteomics) and subsequent acquisition of large-scale datasets consisting of multiple variables. In 'omics technology-based investigations, discrepancies between the number of variables analyzed (e.g., mRNA, proteins, metabolites) and the number of study subjects constitutes a major statistical challenge. The application of traditional univariate statistical methods (e.g., t-test) to these "short-and-wide" datasets may result in high numbers of false positives, while the predominant approach of p-value correction to account for these high false positive rates (e.g., FDR, Bonferroni) are associated with significant losses in statistical power. In other words, the benefit in decreased false positives must be counterbalanced with a concomitant loss in true positives. As an alternative, multivariate statistical analysis (MVA) is increasingly being employed to cope with 'omics-based data structures. When properly applied, MVA approaches can be powerful tools for integration and interpretation of complex 'omics-based datasets towards the goal of identifying biomarkers and/or subphenotypes. However, MVA methods are also prone to over-interpretation and misuse. A common software used in biomedical research to perform MVA-based analyses is the SIMCA package, which includes multiple MVA methods. In this opinion piece, we propose guidelines for minimum reporting standards for a SIMCA-based workflow, in terms of data preprocessing (e.g., normalization, scaling) and model statistics (number of components, R2, Q2, and CV-ANOVA p-value). Examples of these applications in recent COPD and asthma studies are provided. It is expected that readers will gain an increased understanding of the power and utility of MVA methods for applications in biomedical research.

Serum metabolomics reveals pathways and biomarkers associated with asthma pathogenesis

BACKGROUND

Asthma is a chronic inflammatory disease caused by complex interactions of genetic, epigenetic, and environmental factors. For this reason, new approaches are required to clarify the pathogenesis of asthma by systemic review.

OBJECTIVE

We applied a (1)H-NMR metabolomics approach to investigate the altered metabolic pattern in sera from patients with asthma and sought to identify the mechanism underlying asthma and potential biomarkers.

METHOD

A global profile of sera from patients with asthma (n = 39) and controls (n = 26) was generated using (1)H-NMR spectroscopy coupled with multivariate statistical analysis. Endogenous metabolites in serum were rapidly measured using the target-profiling procedure.

RESULTS

Multivariate statistical analysis showed a clear distinction between patients with asthma and healthy subjects. Sera of asthma patients were characterized by increased levels of methionine, glutamine, and histidine and by decreased levels of formate, methanol, acetate, choline, O-phosphocholine, arginine, and glucose. The metabolites detected in the sera of patients with asthma are involved in hypermethylation, response to hypoxia, and immune reaction. Furthermore, the levels of serum metabolites from patients with asthma correlated with asthma severity; in particular, lipid metabolism was altered in patients with lower forced expiratory volume in 1 s percentage (FEV(1)%) predicted values. In addition, potential biomarkers showed strong predictive power in ROC analysis, and the presence of asthma in external validation models was predicted with high accuracy (90.9% for asthma and 100% for control subjects).

CONCLUSION & CLINICAL RELEVANCE

These data showed that (1)H-NMR-based metabolite profiling of serum may be useful for the effective diagnosis of asthma and a further understanding of its pathogenesis.

Recent advances in asthma biomarker research

Asthma is characterized by recurrent and reversible airflow obstruction, which is routinely monitored by history and physical examination, spirometry and home peak flow diaries. As airway inflammation is central to asthma pathogenesis, its monitoring should be part of patient management plans. Fractional exhaled nitric oxide level (FeNO) is the most extensively studied biomarker of airway inflammation, and FeNO references were higher in Chinese (Asians) than Whites. Published evidence was inconclusive as to whether FeNO is a useful management strategy for asthma. Other biomarkers include direct (histamine, methacholine) and indirect (adenosine, hypertonic saline) challenges of bronchial hyperresponsiveness (BHR), induced sputum and exhaled breath condensate (EBC). A management strategy that normalized sputum eosinophils among adult patients resulted in reductions of BHR and asthma exacerbations. However, subsequent adult and pediatric studies failed to replicate these benefits. Asthma phenotypes as defined by inflammatory cell populations in sputum were also not stable over a 12-month period. A recent meta-analysis concluded that induced sputum is not accurate enough to be applied in routine monitoring of childhood asthma. There is poor correlation between biomarkers that reflect different asthma dimensions: spirometry (airway caliber), BHR (airway reactivity) and FeNO or induced sputum (airway inflammation). Lastly, EBC is easily obtained noninvasively by cooling expired air. Many biomarkers ranging from acidity (pH), leukotrienes, aldehydes, cytokines to growth factors have been described. However, significant overlap between groups and technical difficulty in measuring low levels of inflammatory molecules are the major obstacles for EBC research. Metabolomics is an emerging analytical method for EBC biomarkers. In conclusion, both FeNO and induced sputum are useful asthma biomarkers. However, they will only form part of the clinical picture. Longitudinal studies with focused hypotheses and well-designed protocols are needed to establish the roles of these biomarkers in asthma management. The measurement of biomarkers in EBC remains a research tool.

Application of metabolomics approaches to the study of respiratory diseases

Metabolomics is the global unbiased analysis of all the small-molecule metabolites within a biological system, under a given set of conditions. These methods offer the potential for a holistic approach to clinical medicine, as well as improving disease diagnosis and understanding of pathological mechanisms. Respiratory diseases including asthma and chronic obstructive pulmonary disorder are increasing globally, with the latter predicted to become the third leading cause of global mortality by 2020. The root causes for disease onset remain poorly understood and no cures are available. This review presents an overview of metabolomics followed by in-depth discussion of its application to the study of respiratory diseases, including the design of metabolomics experiments, choice of clinical material collected and potentially confounding experimental factors. Particular challenges in the field are presented and placed within the context of the future of the applications of metabolomics approaches to the study of respiratory diseases.

Metabolomics reveals altered metabolic pathways in experimental asthma

Metabolomics refers to the comprehensive analysis of metabolites in biological systems, and has been employed to study patients with asthma based on their urinary metabolite profile. We hypothesize that airway allergic asthma would affect metabolism in the lungs, and could be detected in bronchoalveolar lavage (BAL) fluid (BALF) using a combined liquid chromatography- and gas chromatography-mass spectrometry (MS) platform. The objective of this study was to investigate changes of lung metabolism in allergic asthma by metabolomic analysis of BALF. BALB/c mice were sensitized and challenged with ovalbumin to develop experimental asthma. Dexamethasone was administered to study the effects of corticosteroids on lung metabolism. Metabolites in BALF were measured using liquid chromatography-MS and gas chromatography-MS, and multivariate statistical analysis was performed by orthogonal projections to latent structures discriminant analysis. Metabolomic analysis of BALF from ovalbumin-challenged mice revealed novel changes in metabolic pathways in the lungs as compared with control animals. These metabolite changes suggest alterations of energy metabolism in asthmatic lungs, with increases of lactate, malate, and creatinine and reductions in carbohydrates, such as mannose, galactose, and arabinose. Lipid and sterol metabolism were affected with significant decreases in phosphatidylcholines, diglycerides, triglycerides, cholesterol, cortol, and cholic acid. Dexamethasone treatment effectively reversed many key metabolite changes, but was ineffective in repressing lactate, malate, and creatinine, and induced additional metabolite changes. Metabolomic analysis of BALF offers a promising approach to investigating allergic asthma. Our overall findings revealed considerable pathway changes in lung metabolism in asthmatic lungs, including energy, amino acids, and lipid metabolism.

The metabolomics of asthma: novel diagnostic potential

Asthma is one of the most common chronic illnesses, especially in children. Reaching the diagnosis of asthma and its management are more difficult than for other chronic illnesses. For example, asthma is a heterogeneous syndrome with many clinical classifications based on patient symptoms, lung function, and response to therapy. The symptoms and objective measurements of lung function, often used to guide therapy, are largely based on the inflammation of the airways. Because measuring airway dysfunction and inflammation in a typical clinical setting is difficult, it is often not done. Metabolomics is the study of small molecules generated from cellular metabolic activity. It is possible that the metabolic profile of a patient with a chronic illness such as asthma is different from that of a healthy patient or from a patient with another respiratory illness. Furthermore, if this metabolome could be measured, it might also vary with disease severity. The pattern of metabolites becomes the diagnostic representing the disease. This article outlines the more recent work that has been done to develop the metabolomic profile of asthma.

Metabolomics in paediatric respiratory diseases and bronchiolitis

The metabolic study of an organism may make it possible to monitor, through the metabolites,the physiology and/or pathology of the organism itself. Metabolomics, in the strictest sense, "the set of metabolites, the final products of the genetic expression". Most clinical chemistry tests available today rely on old technologies that measure only a single chemical in blood, urine or other biofluids, and these tests are neither sensitive nor specific for any particular disease. Metabolomics offers a holistic approach to systems medicine, with the promise to enhance clinical chemistry diagnostics in several pathologic conditions. The present review covers the application of clinical metabolomics in three different areas of respiratory diseases in pediatrics: asthma, pneumonia and bronchiolitis. Comparison between two 1H-NMR urine spectra by two bronchiolitis patients are also presented.

Urinary metabolomics in newborns and infants

Metabolomics is a new approach based on the systematic study of the full complement of metabolites in a biological sample. This technology consists of two sequential steps: (1) an experimental technique, based on nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry, designed to profile low-molecular-weight compounds, and (2) multivariate data analysis. The metabolomic analysis of biofluids or tissues has been successfully used in the fields of physiology, diagnostics, functional genomics, pharmacology, toxicology, and nutrition. Recent studies have evaluated how physiological variables or pathological conditions can affect metabolomic profiles of different biofluids in pediatric populations. The overall metabolic status of the neonate is little known. If more information on perinatal/neonatal maturational processes and their metabolic background were available, the management of sick or preterm newborns might be improved. Currently, the use of metabolomics in neonatology is still in the pioneering phase. Meaningful diagnostic information and simple, noninvasive collection techniques make urine a particularly suitable biofluid for metabolomic approach in neonatal medicine, although blood has also been investigated. Different fields of neonatology such as postnatal maturation, asphyxia/hypoxia, inborn errors of metabolism, nutrition, nephrouropathies, nephrotoxicity, cardiovascular diseases, and other conditions have been investigated using a metabolomic approach. Together with genomics and proteomics, metabolomics appears to be a promising tool in neonatology for the monitoring of postnatal metabolic maturation, the identification of biomarkers as early predictors of outcome, the diagnosis and monitoring of various diseases, and the "tailored" management of neonatal disorders.

Metabolic effects of albumin therapy in acute lung injury measured by proton nuclear magnetic resonance spectroscopy of plasma: a pilot study

OBJECTIVE

Improved means to monitor and guide interventions could be useful in the intensive care unit. Metabolomic analysis with bioinformatics is used to understand mechanisms and identify biomarkers of disease development and progression. This pilot study evaluated plasma proton nuclear magnetic resonance spectroscopy as a means to monitor metabolism following albumin administration in acute lung injury patients.

DESIGN

This study was conducted on plasma samples from six albumin-treated and six saline-treated patients from a larger double-blind trial. The albumin group was administered 25 g of 25% human albumin in 0.9% saline every 8 hrs for a total of nine doses over 72 hrs. A 0.9% concentration of saline was used as a placebo. Blood samples were collected immediately before, 1 hr after, and 4 hrs after the albumin/saline administration for the first, fourth, and seventh doses (first dose of each day for 3 days). Samples were analyzed by proton nuclear magnetic resonance spectroscopy, and spectra were analyzed by principal component analysis and biostatistical methods.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

After 1 day of albumin therapy, changes in small molecules, including amino acids and plasma lipids, were evident with principal component analysis. Differences remained 3 days after the last albumin administration. Analysis of data along with spectra from healthy controls showed that spectra for patients receiving albumin had a trajectory toward the spectra observed for healthy individuals while those of the placebo controls did not.

CONCLUSION

The data suggest that metabolic changes detected by proton nuclear magnetic resonance spectroscopy and the bioinformatics tool may be a useful approach to clinical research, especially in acute lung injury.

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.

Human metabolic profiles are stably controlled by genetic and environmental variation

A comprehensive variation map of the human metabolome identifies genetic and stable-environmental sources as major drivers of metabolite concentrations. The data suggest that sample sizes of a few thousand are sufficient to detect metabolite biomarkers predictive of disease.

We designed a longitudinal twin study to characterize the genetic, stable-environmental, and longitudinally fluctuating influences on metabolite concentrations in two human biofluids—urine and plasma—focusing specifically on the representative subset of metabolites detectable by ¹H nuclear magnetic resonance (¹H NMR) spectroscopy.

We identified widespread genetic and stable-environmental influences on the (urine and plasma) metabolomes, with (30 and 42%) attributable on average to familial sources, and (47 and 60%) attributable to longitudinally stable sources.

Ten of the metabolites annotated in the study are estimated to have >60% familial contribution to their variation in concentration.

Our findings have implications for the design and interpretation of ¹H NMR-based molecular epidemiology studies. On the basis of the stable component of variation quantified in the current paper, we specified a model of disease association under which we inferred that sample sizes of a few thousand should be sufficient to detect disease-predictive metabolite biomarkers.

Metabolites are small molecules involved in biochemical processes in living systems. Their concentration in biofluids, such as urine and plasma, can offer insights into the functional status of biological pathways within an organism, and reflect input from multiple levels of biological organization—genetic, epigenetic, transcriptomic, and proteomic—as well as from environmental and lifestyle factors. Metabolite levels have the potential to indicate a broad variety of deviations from the ‘normal' physiological state, such as those that accompany a disease, or an increased susceptibility to disease. A number of recent studies have demonstrated that metabolite concentrations can be used to diagnose disease states accurately. A more ambitious goal is to identify metabolite biomarkers that are predictive of future disease onset, providing the possibility of intervention in susceptible individuals.

If an extreme concentration of a metabolite is to serve as an indicator of disease status, it is usually important to know the distribution of metabolite levels among healthy individuals. It is also useful to characterize the sources of that observed variation in the healthy population. A proportion of that variation—the heritable component—is attributable to genetic differences between individuals, potentially at many genetic loci. An effective, molecular indicator of a heritable, complex disease is likely to have a substantive heritable component. Non-heritable biological variation in metabolite concentrations can arise from a variety of environmental influences, such as dietary intake, lifestyle choices, general physical condition, composition of gut microflora, and use of medication. Variation across a population in stable-environmental influences leads to long-term differences between individuals in their baseline metabolite levels. Dynamic environmental pressures lead to short-term fluctuations within an individual about their baseline level. A metabolite whose concentration changes substantially in response to short-term pressures is relatively unlikely to offer long-term prediction of disease. In summary, the potential suitability of a metabolite to predict disease is reflected by the relative contributions of heritable and stable/unstable-environmental factors to its variation in concentration across the healthy population.

Studies involving twins are an established technique for quantifying the heritable component of phenotypes in human populations. Monozygotic (MZ) twins share the same DNA genome-wide, while dizygotic (DZ) twins share approximately half their inherited DNA, as do ordinary siblings. By comparing the average extent of phenotypic concordance within MZ pairs to that within DZ pairs, it is possible to quantify the heritability of a trait, and also to quantify the familiality, which refers to the combination of heritable and common-environmental effects (i.e., environmental influences shared by twins in a pair). In addition to incorporating twins into the study design, it is useful to quantify the phenotype in some individuals at multiple time points. The longitudinal aspect of such a study allows environmental effects to be decomposed into those that affect the phenotype over the short term and those that exert stable influence.

For the current study, urine and blood samples were collected from a cohort of MZ and DZ twins, with some twins donating samples on two occasions several months apart. Samples were analysed by ¹H nuclear magnetic resonance (¹H NMR) spectroscopy—an untargeted, discovery-driven technique for quantifying metabolite concentrations in biological samples. The application of ¹H NMR to a biological sample creates a spectrum, made up of multiple peaks, with each peak's size quantitatively representing the concentration of its corresponding hydrogen-containing metabolite.

In each biological sample in our study, we extracted a full set of peaks, and thereby quantified the concentrations of all common plasma and urine metabolites detectable by ¹H NMR. We developed bespoke statistical methods to decompose the observed concentration variation at each metabolite peak into that originating from familial, individual-environmental, and unstable-environmental sources.

We quantified the variability landscape across all common metabolite peaks in the urine and plasma ¹H NMR metabolomes. We annotated a subset of peaks with a total of 65 metabolites; the variance decompositions for these are shown in Figure 1. Ten metabolites' concentrations were estimated to have familial contributions in excess of 60%. The average proportion of stable variation across all extracted metabolite peaks was estimated to be 47% in the urine samples and 60% in the plasma samples; the average estimated familiality was 30% for urine and 42% for plasma. These results comprise the first quantitative variation map of the ¹H NMR metabolome. The identification and quantification of substantive widespread stability provides support for the use of these biofluids in molecular epidemiology studies. On the basis of our findings, we performed power calculations for a hypothetical study searching for predictive disease biomarkers among ¹H NMR-detectable urine and plasma metabolites. Our calculations suggest that sample sizes of 2000–5000 should allow reliable identification of disease-predictive metabolite concentrations explaining 5–10% of disease risk, while greater sample sizes of 5000–20 000 would be required to identify metabolite concentrations explaining 1–2% of disease risk.

¹H Nuclear Magnetic Resonance spectroscopy (¹H NMR) is increasingly used to measure metabolite concentrations in sets of biological samples for top-down systems biology and molecular epidemiology. For such purposes, knowledge of the sources of human variation in metabolite concentrations is valuable, but currently sparse. We conducted and analysed a study to create such a resource. In our unique design, identical and non-identical twin pairs donated plasma and urine samples longitudinally. We acquired ¹H NMR spectra on the samples, and statistically decomposed variation in metabolite concentration into familial (genetic and common-environmental), individual-environmental, and longitudinally unstable components. We estimate that stable variation, comprising familial and individual-environmental factors, accounts on average for 60% (plasma) and 47% (urine) of biological variation in ¹H NMR-detectable metabolite concentrations. Clinically predictive metabolic variation is likely nested within this stable component, so our results have implications for the effective design of biomarker-discovery studies. We provide a power-calculation method which reveals that sample sizes of a few thousand should offer sufficient statistical precision to detect ¹H NMR-based biomarkers quantifying predisposition to disease.

Biochemical disorders associated with antiproliferative effect of dehydroepiandrosterone in hepatoma cells as revealed by LC-based metabolomics

DHEA is known to have chemopreventive and antiproliferative activities, and was initially thought to be mediated by inhibition of G6PD. Our previous study has shown that DHEA may act through interference with energy metabolism. To study the effect of pharmacological dose of DHEA on cellular metabolism, and to further delineate the mechanism underlying its antiproliferative effect, we applied a metabolomic approach to globally profile the changes in metabolites in SK-Hep1 cells underexpressing G6PD (Sk-Gi) and control cells (Sk-Sc) after DHEA treatment. RRLC-TOF-MS was used to identify metabolites, and tandem mass spectrometry was used to confirm their identity. DHEA induced changes in glutathione metabolism, lipid metabolism, s-adenosylmethionine (SAM) metabolism, as well as lysine metabolism. Elevation in level of glutathione disulfide, together with a concomitant decrease in level of reduced glutathione, was indicative of increased oxidative stress. Depletion of carnitine and its acyl derivatives reflected decline in fatty acid catabolism. These changes were associated with mitochondrial malfunction and reduction in cellular ATP content. Cardiolipin (CL) and phosphatidylcholine (PC) levels decreased significantly, suggesting that alterations in lipid composition are causally related to decline in mitochondrial function after DHEA treatment. The decline in cellular SAM content was accompanied by decreased expression of methionine adenosyltransferase genes MAT2A and MAT2B. SAM supplementation partially rescued cells from DHEA-induced growth stagnation. Our findings suggest that DHEA causes perturbation of multiple pathways in cellular metabolism. Decreased SAM production, and cardiolipin depletion and the resulting mitochondrial dysfunction underlie the antiproliferative effect of DHEA.

Metabolomic profiling of asthma: diagnostic utility of urine nuclear magnetic resonance spectroscopy

BACKGROUND

The ability to diagnose and monitor asthma on the basis of noninvasive measurements of airway cellular dysfunction is difficult in the typical clinical setting.

OBJECTIVE

Metabolomics is the study of molecules created by cellular metabolic pathways. We hypothesized that the metabolic activity of children with asthma would differ from healthy children without asthma. Furthermore, children having an asthma exacerbation would be different compared with children with stable asthma in outpatient clinics. Finally, we hypothesized that (1)H-nuclear magnetic resonance (NMR) would measure such differences using urine samples, one of the least invasive forms of biofluid sampling.

METHODS

Children (135 total, ages 4-16 years) were enrolled, having met the criteria of healthy controls (C), stable asthma in the outpatient clinic (AO), or unstable asthma in the emergency department (AED). Partial least squares discriminant analysis was performed on the NMR data to create models of separation (70 metabolites were measured/urine sample). Some NMR data were withheld from modeling to be run blindly to determine possible diagnostic accuracy.

RESULTS

On the basis of the model of AO versus C, 31 of 33 AO samples were correctly diagnosed with asthma (94% accuracy). Only 1 of 20 C samples was incorrectly labeled as asthma (5% misclassification). On the basis of the AO versus AED model, 31 of the 33 AO samples were correctly diagnosed as outpatient asthma (94% accurate).

CONCLUSION

This is the first report suggesting that (1)H-NMR analysis of human urine samples has the potential to be a useful clinical tool for physicians treating asthma.

Metabolomics applied to urine samples in childhood asthma; differentiation between asthma phenotypes and identification of relevant metabolites

Asthma is a heterogeneous disorder and one of the most common chronic childhood diseases. An improved characterization of asthma phenotypes would be invaluable for the understanding of the pathogenic mechanisms and the correct treatment of this disease. The aim of this pilot study was to explore the potential of metabolomics applied to urine samples in characterizing asthma, and to identify the most representative metabolites. Urine samples of 41 atopic asthmatic children (further subdivided in sub-groups according to the symptoms) and 12 age-matched controls were analyzed. Untargeted metabolic profiles were collected by LC-MS, and studied by multivariate analysis. The group of the asthmatics was differentiated by a model that proved to be uncorrelated with the chronic assumption of controller drugs on the part of the patients. The distinct sub-groups were also appropriately modeled. Further investigations revealed a reduced excretion of urocanic acid, methyl-imidazoleacetic acid and a metabolite resembling the structure of an Ile-Pro fragment in the asthmatics. The meaning of these findings was discussed and mainly correlated with the modulation of immunity in asthma. Metabolic profiles from urines have revealed the potential to characterize asthma and enabled the identification of metabolites that may have a role in the underlying inflammation.

Exploring airway diseases by NMR-based metabonomics: a review of application to exhaled breath condensate

There is increasing evidence that biomarkers of exhaled gases or exhaled breath condensate (EBC) may help in detecting abnormalities in respiratory diseases mirroring increased, oxidative stress, airways inflammation and endothelial dysfunction. Beside the traditional techniques to investigate biomarker profiles, "omics" sciences have raised interest in the clinical field as potentially improving disease phenotyping. In particular, metabonomics appears to be an important tool to gain qualitative and quantitative information on low-molecular weight metabolites present in cells, tissues, and fluids. Here, we review the potential use of EBC as a suitable matrix for metabonomic studies using nuclear magnetic resonance (NMR) spectroscopy. By using this approach in airway diseases, it is now possible to separate specific EBC profiles, with implication in disease phenotyping and personalized therapy.

GC-TOF/MS-based metabolomic profiling of estrogen deficiency-induced obesity in ovariectomized rats

AIM

To explore the alteration of endogenous metabolites and identify potential biomarkers using metabolomic profiling with gas chromatography coupled a time-of-flight mass analyzer (GC/TOF-MS) in a rat model of estrogen-deficiency-induced obesity.

METHODS

Twelve female Sprague-Dawley rats six month of age were either sham-operated or ovariectomized (OVX). Rat blood was collected, and serum was analyzed for biomarkers using standard colorimetric methods with commercial assay kits and a metabolomic approach with GC/TOF-MS. The data were analyzed using multivariate statistical techniques.

RESULTS

A high body weight and body mass index inversely correlated with serum estradiol (E2) in the OVX rats compared to the sham rats. Estrogen deficiency also significantly increased serum total cholesterol, triglycerides, and low-density lipoprotein cholesterol. Utilizing GC/TOF-MS-based metabolomic analysis and the partial least-squares discriminant analysis, the OVX samples were discriminated from the shams. Elevated levels of cholesterol, glycerol, glucose, arachidonic acid, glutamic acid, glycine, and cystine and reduced alanine levels were observed. Serum glucose metabolism, energy metabolism, lipid metabolism, and amino acid metabolism were involved in estrogen-deficiency-induced obesity in OVX rats.

CONCLUSION

The series of potential biomarkers identified in the present study provided fingerprints of rat metabolomic changes during obesity and an overview of multiple metabolic pathways during the progression of obesity involving glucose metabolism, lipid metabolism, and amino acid metabolism.

What's new in asthma pathophysiology and immunopathology?

Research on asthma pathophysiology over the past decade has expanded the complex repertoire involved in the pathophysiology of asthma to include inflammatory, immune and structural cells, as well as a wide range of mediators. Studies have identified a role for connective and other mesenchymal tissues involved in airway remodeling. Recent findings have implicated the innate immune response in asthma and have revealed interesting patterns of interaction between the innate and adaptive immune response and the associated complex chronic inflammatory reaction. New immune cell populations have also been added to this repertoire, including Tregs, natural killer T cells and Th17 cells. The role of the eosinophil, a prominent pathological feature in most asthma phenotypes, has also been expanding to include roles such as tissue modifiers and immune regulators via a number of fascinating and hitherto unexplored mechanistic pathways. In addition, new and significant roles have been proposed for airway smooth muscle cells, fibroblasts, epithelial and endothelial cells. Tissue remodeling is now considered an integral element of asthma pathophysiology. Finally, an intricate network of mediators, released from both immune and inflammatory cells, including thymus stromal lymphopoietin and matrix metalloproteinases, have added to the complex milieu of asthma immunity and inflammation. These findings have implications for therapy and the search for novel strategies towards better disease management. Sadly, and perhaps due to the complex nature of asthma, advances in therapeutic discoveries and developments have been limited. Thus, understanding the precise roles played by the numerous dramatis personae in this odyssey, both individually and collectively within the context of asthma pathophysiology, continues to pose new challenges. It is clear that the next stage in this saga is to embark on studies that transcend reductionist approaches to involve system analysis of the complex and multiple variables involved in asthma, including the need to narrow down the phenotypes of this condition based on careful analysis of the organs (lung and airways), cells, mediators and other factors involved in bronchial asthma.

Advances in the understanding of pathogenesis, and diagnostics and therapeutics for feline allergic asthma

Asthma is a common inflammatory disease of the lower airways and is believed to be of allergic etiology in cats. As little progress has been made in establishing rigorous criteria to differentiate it from other inflammatory lower airway diseases such as chronic bronchitis, descriptions of 'asthma' in the literature have often been inaccurate, grouping this syndrome with other feline airway diseases. With the development of more sensitive and specific diagnostics, it will become easier to distinguish asthma as a disease entity. Pulmonary function testing with bronchoprovocation/bronchodilator responsiveness trials and biomarkers hold particular promise. Discrimination is of critical importance as targeted therapies for the allergic inflammatory cascade are developed and become available for therapeutic trials in pet cats.

Genomic and metabolomic advances in the identification of disease and adverse event biomarkers

Incomplete knowledge of tissue pathogenesis is hampering the identification of biomarkers for the appropriate therapeutic targets to prevent or inhibit disease processes, and the prediction and diagnosis of injury due to disease and adverse events of drug therapy. The revolution in genomics and metabolomics, combined with advanced bioinformatics and computational methods for mining such large, complex data sets, are beginning to provide critical insights into tissue injury. Such results will move us closer to the promise of personalized medicine.

Metabolomic analysis of human disease and its application to the eye

Metabolomics, the analysis of the metabolite profile in body fluids or tissues, is being applied to the analysis of a number of different diseases as well as being used in following responses to therapy. While genomics involves the study of gene expression and proteomics the expression of proteins, metabolomics investigates the consequences of the activity of these genes and proteins. There is good reason to think that metabolomics will find particular utility in the investigation of inflammation, given the multi-layered responses to infection and damage that are seen. This may be particularly relevant to eye disease, which may have tissue specific and systemic components. Metabolomic analysis can inform us about ocular or other body fluids and can therefore provide new information on pathways and processes involved in these responses. In this review, we explore the metabolic consequences of disease, in particular ocular conditions, and why the data may be usefully and uniquely assessed using the multiplexed analysis inherent in the metabolomic approach.