1H NMR-based metabolomic study of metabolic profiling for systemic lupus erythematosus

Emerging role of metabolomics in rheumatology

The pursuit for understanding disease pathogenesis, in this age of rapid laboratory diagnostics and fast-paced research, has led scientists worldwide to take recourse in hypothesis-free approaches for molecular diagnosis. Metabolomics is one such powerful tool that explores comprehensibly the metabolic alternations in human diseases. It involves study of small molecules of less than 1 kD in size by either LSMS or nuclear magnetic resonance. Unlike genomics, which tells us what may have happened, metabolomics reflects what did happen. The NMR technique has an advantage of analyzing metabolites without sample preparation, thereby diminishing artifacts, is less cumbersome and with the latest database on Metabolome; about 30 000 metabolites can be identified. The study of metabolomics for several rheumatic diseases, including rheumatoid arthritis, lupus, osteoarthritis and vasculitis, has revealed distinctive metabolic signatures. Thus, metabolomics is a technique that promises precision medicine with better biomarkers, robust predictors of drug response and of disease outcome, discovery of newer metabolites and pathways in disease pathogenesis, and finally, targeted drug development. This review intends to decipher its relevance in common rheumatic diseases.

NMR-Based Serum Metabolomics Revealed Distinctive Metabolic Patterns in Reactive Arthritis Compared with Rheumatoid Arthritis

Reactive arthritis (ReA) is a member of seronegative spondyloarthropathy (SSA), which involves an acute/subacute onset of asymmetrical lower limb joint inflammation weeks after a genitourinary/gastrointestinal infection. The diagnosis is clinical because it is difficult to culture the microbes from synovial fluid. Arthritis patients with a similar clinical picture but lapsed history of an immediate preceding infection that do not fulfill the diagnostic criteria of other members of SSA, such as ankylosing spondylitis, psoriatic arthritis, and arthritis associated with inflammatory bowel disease, are labeled as peripheral undifferentiated spondyloarthropathy (uSpA). Both ReA and uSpA patients show a strong association with class I major histocompatibility complex allele, HLA-B27, and a clear association with an infectious trigger; however, the disease mechanism is far from clear. Because the clinical picture is largely dominated by rheumatoid-arthritis (RA)-like features including elevated levels of inflammatory markers (such as ESR, CRP, etc.), these overlapping symptoms often confound the clinical diagnosis and represent a clinical dilemma, making treatment choice more generalized. Therefore, there is a compelling need to identify biomarkers that can support the diagnosis of ReA/uSpA. In the present study, we performed NMR-based serum metabolomics analysis and demonstrated that ReA/uSpA patients are clearly distinguishable from controls and further that these patients can also be distinguished from the RA patients based on the metabolic profiles, with high sensitivity and specificity. The discriminatory metabolites were further subjected to area under receiver operating characteristic curve analysis, which led to the identification of four metabolic entities (i.e., valine, leucine, arginine/lysine, and phenylalanine) that could differentiate ReA/uSpA from RA.

Metabolomic profile of systemic sclerosis patients

Systemic sclerosis (SSc) is an autoimmune disease of unknown aetiology characterized by vascular lesions, immunological alterations and diffuse fibrosis of the skin and internal organs. Since recent evidence suggests that there is a link between metabolomics and immune mediated disease, serum metabolic profile of SSc patients and healthy controls was investigated by ¹H-NMR and GC-MS techniques. The results indicated a lower level of aspartate, alanine, choline, glutamate, and glutarate in SSc patients compared with healthy controls. Moreover, comparing patients affected by limited SSc (lcSSc) and diffuse SSc (dcSSc), 6 discriminant metabolites were identified. The multivariate analysis performed using all the metabolites significantly different revealed glycolysis, gluconeogenesis, energetic pathways, glutamate metabolism, degradation of ketone bodies and pyruvate metabolism as the most important networks. Aspartate, alanine and citrate yielded a high area under receiver-operating characteristic (ROC) curves (AUC of 0.81; CI 0.726–0.93) for discriminating SSc patients from controls, whereas ROC curve generated with acetate, fructose, glutamate, glutamine, glycerol and glutarate (AUC of 0.84; CI 0.7–0.98) discriminated between lcSSc and dcSSc. These results indicated that serum NMR-based metabolomics profiling method is sensitive and specific enough to distinguish SSc from healthy controls and provided a feasible diagnostic tool for the diagnosis and classification of the disease.

Analysis of the free fatty acid metabolome in the plasma of patients with systemic lupus erythematosus and fever

INTRODUCTION

Systemic lupus erythematosus (SLE) is a multifactorial autoimmune disease with heterogeneous clinical manifestations mediated by immune dysregulation.

OBJECTIVES

We aimed to analyze the metabolomic differences in free fatty acids (FFAs) between patients with SLE and healthy controls (HCs).

METHODS

In this study, the levels of 24 FFAs, as their tert-butyldimethylsilyl derivatives, in the plasma of 41 patients with SLE and 41 HCs, were investigated using gas chromatography with mass spectrometry in selected-ion monitoring mode.

RESULTS

The results showed that patients with SLE and HCs had significantly different levels of 13 of the 24 FFAs. The levels of myristic, palmitoleic, oleic, and eicosenoic acids were significantly higher, whereas the levels of caproic, caprylic, linoleic, stearic, arachidonic, eicosanoic, behenic, lignoceric, and hexacosanoic acids were significantly lower in patients with SLE, than in the HCs. In the partial-correlation analysis of the FFA profiles and markers of disease activity of SLE, several metabolic markers correlated with SLE disease activity.

CONCLUSIONS

Our results provide a comprehensive understanding of the relationship between FFAs and markers of SLE disease activity. Thus, this approach has promising potential for the discovery of metabolic biomarkers of SLE.

Critical review of reporting of the data analysis step in metabolomics

INTRODUCTION

We present the first study to critically appraise the quality of reporting of the data analysis step in metabolomics studies since the publication of minimum reporting guidelines in 2007.

OBJECTIVES

The aim of this study was to assess the standard of reporting of the data analysis step in metabolomics biomarker discovery studies and to investigate whether the level of detail supplied allows basic understanding of the steps employed and/or reuse of the protocol. For the purposes of this review we define the data analysis step to include the data pretreatment step and the actual data analysis step, which covers algorithm selection, univariate analysis and multivariate analysis.

METHOD

We reviewed the literature to identify metabolomic studies of biomarker discovery that were published between January 2008 and December 2014. Studies were examined for completeness in reporting the various steps of the data pretreatment phase and data analysis phase and also for clarity of the workflow of these sections.

RESULTS

We analysed 27 papers, published anytime in 2008 until the end of 2014 in the area or biomarker discovery in serum metabolomics. The results of this review showed that the data analysis step in metabolomics biomarker discovery studies is plagued by unclear and incomplete reporting. Major omissions and lack of logical flow render the data analysis' workflows in these studies impossible to follow and therefore replicate or even imitate.

CONCLUSIONS

While we await the holy grail of computational reproducibility in data analysis to become standard, we propose that, at a minimum, the data analysis section of metabolomics studies should be readable and interpretable without omissions such that a data analysis workflow diagram could be extrapolated from the study and therefore the data analysis protocol could be reused by the reader. That inconsistent and patchy reporting obfuscates reproducibility is a given. However even basic understanding and reuses of protocols are hampered by the low level of detail supplied in the data analysis sections of the studies that we reviewed.

Omics studies: their use in diagnosis and reclassification of SLE and other systemic autoimmune diseases

Omics studies of systemic autoimmune diseases (SADs) in general, and SLE in particular, have delivered isolated information from transcriptome, epigenome, genome, cytokine and metabolome analyses. Such analyses have resulted in the identification of disease susceptibility genes and the description of IFN expression signatures, allowing extensive insight into the mechanisms of disease and the development of new therapies. Access to such technologies allows the recognition of patterns of disease at a pathway level, thereby, to reclassify SLE and other SADs and to develop new therapeutics from a personalized perspective. The use of omic information allows the discovery of correlative patterns involving drugs not currently suspected to be of value in SADs. In this review, we summarize the omics findings for SLE and propose ways of using the data for the identification of new biomarkers, finding new drugs and reclassifying patients not only with SLE, but also with other SADs.

Diagnostic and prognostic tests in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune inflammatory disease characterized by autoantibodies directed against numerous self-nuclear antigens. Because of the heterogeneous nature of lupus, it has been challenging to identify markers that are sensitive and specific enough for its diagnosis and monitoring. However, with the sequencing of the human genome, rapid development of high-throughput approaches has allowed for a better understanding of the etiopathogenesis of complex diseases, including SLE. Here we present a review of the latest advancements in biomarker discovery during the "omics" era, using these novel technologies, for assisting in the diagnosis and prognosis of patients with SLE.

Angiotensin type 1 receptor A1166C polymorphism and systemic lupus erythematosus: correlation with cellular immunity and oxidative stress markers

Angiotensin II, one of the rennin–angiotensin system components, is important in the cardiovascular hemodynamic and plays an important role in the development of cardiovascular disease in systemic lupus erythematosus (SLE) patients. The angiotensin II, through interaction with angiotensin II type 1 receptor (AGTR1), promotes proliferation, inflammation and fibrosis. The single nucleotide polymorphism of the AGTR1 (dbSNP: rs5186) gene can be associated with development and progression of SLE disease. The aims of this study were to compare the frequency of AGTR1 rs5186 in SLE patients with healthy individuals and to evaluate possible association between AGTR1 A1166C gene polymorphism and serum level of lipids, neopterin and malondialdehyde in SLE patients from a population of West Iran. One hundred SLE patients and 98 healthy subjects were studied. The AGTR1 A1166C polymorphism was detected by polymerase chain reaction– restriction fragment length polymorphism method and the serum lipid profile was obtained by enzymatic method. Neopterin and malondialdehyde were detected using high-performance liquid chromatography. We did not detect significant association between AGTR1 A1166C polymorphism and the risk of SLE. The levels of triglyceride (225 ± 118 mg/dl), neopterin (30 ± 24 nmol/l) and malondialdehyde (25 ± 9.6 nmol/l) in SLE patients were significantly higher than those in control subjects (139 ± 56 mg/dl, p = 0.03, 6.4 ± 2, p = 0.03, 9.4 ± 2.5 nmol/l, p = 0.01, respectively). Individuals with AGTR1 AC + CC genotype had higher levels of total cholesterol and malondialdehyde compared with those with AGTR1 AA genotype. SLE patients with either AGTR1 AA or AGTR1AC + CC genotype had significantly higher malondialdehyde or neopterin levels compared with the corresponding control subjects. In conclusion, although the present study did not find any association between AGTR1 A1166C polymorphism and the risk of SLE, the presence of this polymorphism was associated with higher levels of malondialdehyde and higher concentration of neopterin in patients.

Metabolic profiling reveals new serum biomarkers of lupus nephritis

Metabolomics has been applied to explore altered metabolite profiles in disease and identify unique metabolic signatures specific to certain pathologies. The aim of the current study is to characterize the metabolic profile of patients diagnosed with lupus nephritis (LN) and explore new insights into underlying disease processes. A metabolomic approach using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS) was developed in serum samples from 32 LN patients, 30 idiopathic nephrotic syndrome (INS) patients and 28 healthy controls (HCs). Potential biomarkers were screened from orthogonal projection to latent structures discriminate analysis (OPLS-DA) and further evaluated by receiver operating characteristic analysis (ROC). A total of 14 potential biomarkers were screened and tentatively identified for LN patients compared to HCs. Compared to HCs and INS patients, the LN patients had increased serum levels of sorbitol and glycocholic acid metabolites and decreased levels of cortisol, creatinine and L-aspartyl-L-phenylalanine. A panel of three metabolomics (theophylline, oxidized glutathione and capric acid) was identified as biomarkers of LN with a sensitivity of 87.50% and a specificity of 67.86% using ROC analysis. Our results suggest that UPLC-HRMS based quantification of circulating metabolites was a useful tool for identification of biomarkers with the ability to segregate LN patients from INS patients and HCs. The potential biomarkers indicated that the LN metabolic disturbance may be closely associated with inflammation injury, oxidative stress and phospholipid metabolism.

Mixing omics: combining genetics and metabolomics to study rheumatic diseases

Metabolomics is an exciting field in systems biology that provides a direct readout of the biochemical activities taking place within an individual at a particular point in time. Metabolite levels are influenced by many factors, including disease status, environment, medications, diet and, importantly, genetics. Thanks to their dynamic nature, metabolites are useful for diagnosis and prognosis, as well as for predicting and monitoring the efficacy of treatments. At the same time, the strong links between an individual's metabolic and genetic profiles enable the investigation of pathways that underlie changes in metabolite levels. Thus, for the field of metabolomics to yield its full potential, researchers need to take into account the genetic factors underlying the production of metabolites, and the potential role of these metabolites in disease processes. In this Review, the methodological aspects related to metabolomic profiling and any potential links between metabolomics and the genetics of some of the most common rheumatic diseases are described. Links between metabolomics, genetics and emerging fields such as the gut microbiome and proteomics are also discussed.

Metabolic responses to intestine regeneration in sea cucumbers Apostichopus japonicus

Sea cucumbers are excellent models for studying organ regeneration due to their striking capacity to regenerate most of their viscera after evisceration. In this study, we applied NMR-based metabolomics to determine the metabolite changes that occur during the process of intestine regeneration in sea cucumbers. Partial least-squares discriminant analysis showed that there was significant differences in metabolism between regenerative intestines at 3, 7, and 14days post evisceration (dpe) and normal intestines. Changes in the concentration of 13 metabolites related to regeneration were observed and analyzed. These metabolites included leucine, isoleucine, valine, arginine, glutamate, hypotaurine, dimethylamine, N,N-dimethylglycine, betaine, taurine, inosine, homarine, and histidine. Three important genes (betaine-aldehyde dehydrogenase, betaine-homocysteine S-methyltransferase 1, and dimethylglycine dehydrogenase) were differentially expressed to regulate the levels of betaine and N,N-dimethylglycine during intestine regeneration. These results provide an important basis for studying regenerative mechanisms and developing regenerative matrixes.

Metabolites as biomarkers of adverse reactions following vaccination: A pilot study using nuclear magnetic resonance metabolomics

An Adverse Event Following Immunization (AEFI) is an adverse reaction to a vaccination that goes above and beyond the usual side effects associated with vaccinations. One serious AEFI related to the smallpox vaccine is myopericarditis. Metabolomics involves the study of the low molecular weight metabolite profile of cells, tissues, and biological fluids, and provides a functional readout of the phenotype. Metabolomics may help identify a particular metabolic signature in serum of subjects who are predisposed to developing AEFIs. The goal of this study was to identify metabolic markers that may predict the development of adverse events following smallpox vaccination. Serum samples were collected from military personnel prior to and following receipt of smallpox vaccine. The study population included five subjects who were clinically diagnosed with myopericarditis, 30 subjects with asymptomatic elevation of troponins, and 31 subjects with systemic symptoms following immunization, and 34 subjects with no AEFI, serving as controls. Two-hundred pre- and post-smallpox vaccination sera were analyzed by untargeted metabolomics using ¹H nuclear magnetic resonance (NMR) spectroscopy. Baseline (pre-) and post-vaccination samples from individuals who experienced clinically verified myocarditis or asymptomatic elevation of troponins were more metabolically distinguishable pre- and post-vaccination compared to individuals who only experienced systemic symptoms, or controls. Metabolomics profiles pre- and post-receipt of vaccine differed substantially when an AEFI resulted. This study is the first to describe pre- and post-vaccination metabolic profiles of subjects who developed an adverse event following immunization. The study demonstrates the promise of metabolites for determining mechanisms associated with subjects who develop AEFI and the potential to develop predictive biomarkers.

Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics

INTRODUCTION

Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput 'omics' technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used.

OBJECTIVES

We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics.

METHODS

Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC-TOF-MS). For each batch OPLS-DA^® was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile.

RESULTS

A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE.

CONCLUSION

Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation.

NMR based serum metabolomics reveals a distinctive signature in patients with Lupus Nephritis

Management of patient with Lupus Nephritis (LN) continues to remain a challenge for the treating physicians because of considerable morbidity and even mortality. The search of biomarkers in serum and urine is a focus of researchers to unravel new targets for therapy. In the present study, the utility of NMR-based serum metabolomics has been evaluated for the first time in discriminating LN patients from non-nephritis lupus patients (SLE) and further to get new insights into the underlying disease processes for better clinical management. Metabolic profiling of sera obtained from 22 SLE patients, 40 LN patients and 30 healthy controls (HC) were performed using high resolution 1D ¹H-CPMG and diffusion edited NMR spectra to identify the potential molecular biomarkers. Using multivariate analysis, we could distinguish SLE and LN patients from HC and LN from SLE patients. Compared to SLE patients, the LN patients had increased serum levels of lipid metabolites (including LDL/VLDL lipoproteins), creatinine and decreased levels of acetate. Our results revealed that metabolic markers especially lipids and acetate derived from NMR spectroscopy has high sensitivity and specificity to distinguish LN among SLE patients and has the potential to be a useful adjunctive tool in diagnosis and clinical management of LN.

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis

In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA) respond insufficiently to TNF-α inhibitors (TNFis). The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients’ response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124). The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI). The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28), erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP). Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6), sn1-LPC(15:0), ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01). The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23) and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05). Our study established an accurate prediction model for response to TNFi therapy, containing metabolites and clinical parameters. Associations between metabolites and disease activity may help elucidate additional pathologic mechanisms behind RA.

Urine metabolome profiling of immune-mediated inflammatory diseases

BACKGROUND

Immune-mediated inflammatory diseases (IMIDs) are a group of complex and prevalent diseases where disease diagnostic and activity monitoring is highly challenging. The determination of the metabolite profiles of biological samples is becoming a powerful approach to identify new biomarkers of clinical utility. In order to identify new metabolite biomarkers of diagnosis and disease activity, we have performed the first large-scale profiling of the urine metabolome of the six most prevalent IMIDs: rheumatoid arthritis, psoriatic arthritis, psoriasis, systemic lupus erythematosus, Crohn's disease, and ulcerative colitis.

METHODS

Using nuclear magnetic resonance, we analyzed the urine metabolome in a discovery cohort of 1210 patients and 100 controls. Within each IMID, two patient subgroups were recruited representing extreme disease activity (very high vs. very low). Metabolite association analysis with disease diagnosis and disease activity was performed using multivariate linear regression in order to control for the effects of clinical, epidemiological, or technical variability. After multiple test correction, the most significant metabolite biomarkers were validated in an independent cohort of 1200 patients and 200 controls.

RESULTS

In the discovery cohort, we identified 28 significant associations between urine metabolite levels and disease diagnosis and three significant metabolite associations with disease activity (P FDR < 0.05). Using the validation cohort, we validated 26 of the diagnostic associations and all three metabolite associations with disease activity (P FDR < 0.05). Combining all diagnostic biomarkers using multivariate classifiers we obtained a good disease prediction accuracy in all IMIDs and particularly high in inflammatory bowel diseases. Several of the associated metabolites were found to be commonly altered in multiple IMIDs, some of which can be considered as hub biomarkers. The analysis of the metabolic reactions connecting the IMID-associated metabolites showed an over-representation of citric acid cycle, phenylalanine, and glycine-serine metabolism pathways.

CONCLUSIONS

This study shows that urine is a source of biomarkers of clinical utility in IMIDs. We have found that IMIDs show similar metabolic changes, particularly between clinically similar diseases and we have found, for the first time, the presence of hub metabolites. These findings represent an important step in the development of more efficient and less invasive diagnostic and disease monitoring methods in IMIDs.

Metabolic Profiling of Systemic Lupus Erythematosus and Comparison with Primary Sjögren's Syndrome and Systemic Sclerosis

Systemic lupus erythematosus (SLE) is a chronic inflammatory autoimmune disease which can affect most organ systems including skin, joints and the kidney. Clinically, SLE is a heterogeneous disease and shares features of several other rheumatic diseases, in particular primary Sjögrens syndrome (pSS) and systemic sclerosis (SSc), why it is difficult to diagnose The pathogenesis of SLE is not completely understood, partly due to the heterogeneity of the disease. This study demonstrates that metabolomics can be used as a tool for improved diagnosis of SLE compared to other similar autoimmune diseases. We observed differences in metabolic profiles with a classification specificity above 67% in the comparison of SLE with pSS, SSc and a matched group of healthy individuals. Selected metabolites were also significantly different between studied diseases. Biochemical pathway analysis was conducted to gain understanding of underlying pathways involved in the SLE pathogenesis. We found an increased oxidative activity in SLE, supported by increased xanthine oxidase activity and an increased turnover in the urea cycle. The most discriminatory metabolite observed was tryptophan, with decreased levels in SLE patients compared to control groups. Changes of tryptophan levels were related to changes in the activity of the aromatic amino acid decarboxylase (AADC) and/or to activation of the kynurenine pathway.

Metabolomics in rheumatic diseases: desperately seeking biomarkers

Metabolomics enables the profiling of large numbers of small molecules in cells, tissues and biological fluids. These molecules, which include amino acids, carbohydrates, lipids, nucleotides and their metabolites, can be detected quantitatively. Metabolomic methods, often focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry, have potential for early diagnosis, monitoring therapy and defining disease pathogenesis in many therapeutic areas, including rheumatic diseases. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanisms are being revealed and are shaping our understanding of cell biology, physiology and medicine. These pathways can potentially be targeted to diagnose and treat patients with immune-mediated diseases.

Metabolomics to identify biomarkers and as a predictive tool in inflammatory diseases

There is an overwhelming need for a simple, reliable tool that aids clinicians in diagnosing, assessing disease activity and treating rheumatic conditions. Identification of biomarkers in partially understood inflammatory disorders has long been sought after as the Holy Grail of Rheumatology. Given the complex nature of inflammatory conditions, it has been difficult to earmark the potential biomarkers. Metabolomics, however, is promising in providing new insights into inflammatory conditions and also identifying such biomarkers. Metabolomic studies have generally revealed increased energy requirements for by-products of a hypoxic environment, leading to a characteristic metabolic fingerprint. Here, we discuss the significance of such studies and their potential as a biomarker.

Metabolic profiling of residents in the vicinity of a petrochemical complex

No previous studies have simultaneously measured the biomarkers of environmental exposure and metabolome perturbation in residents affected by industrial pollutants. This study aimed to investigate the metabolic effects of environmental pollutants such as vanadium and polycyclic aromatic hydrocarbons (PAHs) on residents in the vicinity of a petrochemical complex. The study subjects were 160 residents, including 80 high-exposure subjects exposed to high levels of vanadium and PAHs and 80 age- and gender-matched low-exposure subjects living within a 40-km radius of a petrochemical complex. The exposure biomarkers vanadium and 1-hydroxypyrene and four oxidative/nitrosative stress biomarkers were measured in these subjects. Plasma samples from the study subjects were also analyzed using (1)H NMR spectroscopy for metabolic profiling. The results showed that the urinary levels of vanadium and 1-hydroxypyrene in the high-exposure subjects were 40- and 20-fold higher, respectively, than those in the low-exposure subjects. Higher urinary levels of stress biomarkers, including 8-OHdG, HNE-MA, 8-isoPF2α, and 8-NO2Gua, were also observed among the high-exposure subjects compared with the low-exposure subjects. Partial least squares discriminant analysis of the plasma metabolome demonstrated a clear separation between the high- and low-exposure subjects; the intensities of amino acids and carbohydrate metabolites were lower in the high-exposure subjects compared with the low-exposure subjects. The exposure to vanadium and PAHs may cause a reduction in the levels of amino acids and carbohydrates by elevating PPAR and insulin signaling, as well as oxidative/nitrosative stress.

A Systems Biology-Based Investigation into the Pharmacological Mechanisms of Sheng-ma-bie-jia-tang Acting on Systemic Lupus Erythematosus by Multi-Level Data Integration

Sheng-ma-bie-jia-tang (SMBJT) is a Traditional Chinese Medicine (TCM) formula that is widely used for the treatment of Systemic Lupus Erythematosus (SLE) in China. However, molecular mechanism behind this formula remains unknown. Here, we systematically analyzed targets of the ingredients in SMBJT to evaluate its potential molecular mechanism. First, we collected 1,267 targets from our previously published database, the Traditional Chinese Medicine Integrated Database (TCMID). Next, we conducted gene ontology and pathway enrichment analyses for these targets and determined that they were enriched in metabolism (amino acids, fatty acids, etc.) and signaling pathways (chemokines, Toll-like receptors, adipocytokines, etc.). 96 targets, which are known SLE disease proteins, were identified as essential targets and the rest 1,171 targets were defined as common targets of this formula. The essential targets directly interacted with SLE disease proteins. Besides, some common targets also had essential connections to both key targets and SLE disease proteins in enriched signaling pathway, e.g. toll-like receptor signaling pathway. We also found distinct function of essential and common targets in immune system processes. This multi-level approach to deciphering the underlying mechanism of SMBJT treatment of SLE details a new perspective that will further our understanding of TCM formulas.

¹H-NMR-Based Metabolomic Study for Identifying Serum Profiles Associated with the Response to Etanercept in Patients with Rheumatoid Arthritis

OBJECTIVE

A considerable proportion of patients with rheumatoid arthritis (RA) do not have a satisfactory response to biological therapies. We investigated the use of metabolomics approach to identify biomarkers able to anticipate the response to biologics in RA patients.

METHODS

Due to gender differences in metabolomic profiling, the analysis was restricted to female patients starting etanercept as the first biological treatment and having a minimum of six months' follow-up. Each patient was evaluated by the same rheumatologist before and after six months of treatment. At this time, the clinical response (good, moderate, none) was determined according to the EUropean League Against Rheumatism (EULAR) criteria, based on both erythrocyte sedimentation rate (EULAR-ESR) and C-reactive protein (EULAR-CRP). Sera collected prior and after six months of etanercept were analyzed by 1H-nuclear magnetic resonance (NMR) spectroscopy in combination with multivariate data analysis.

RESULTS

Twenty-seven patients were enrolled: 18 had a good/moderate response and 9 were non responders according to both EULAR-ESR and EULAR-CRP after six months of etanercept. Metabolomic analysis at baseline was able to discriminate good, moderate, and non-responders with a very good predictivity (Q2 = 0.68) and an excellent sensitivity, specificity, and accuracy (100%). In good responders, we found an increase in isoleucine, leucine, valine, alanine, glutamine, tyrosine, and glucose levels and a decrease in 3-hydroxybutyrate levels after six months of treatment with etanercept with respect to baseline.

CONCLUSION

Our study confirms the potential of metabolomic analysis to predict the response to biological agents. Changes in metabolic profiles during treatment may help elucidate their mechanism of action.

Metabonomics and systems biology

Systems biology represents an integrative research strategy that studies the interactions between DNA, mRNA, protein, and metabolite level in an organism, thereby including the interactions with the physical environment and other organisms. The application of metabonomics, or the quantitative study of metabolites in biological systems, in systems biology is currently an emerging area of research, which can contribute to the discovery of (disease) signatures, drug targeting and design, and the further elucidation of basic and more complex biochemical principles. This chapter covers the contribution of metabonomics in advancing our understanding in systems biology.

NMR-Based Serum Metabolomics Discriminates Takayasu Arteritis from Healthy Individuals: A Proof-of-Principle Study

Takayasu arteritis (TA) is a debilitating, systemic disease that involves the aorta and large arteries in a chronic inflammatory process that leads to vessel stenosis. Initially, the disease remains clinically silent (or remains undetected) until the patients present with vascular occlusion. Therefore, new methods for appropriate and timely diagnosis of TA cases are needed to start proper therapy on time and also to monitor the patient's response to the given treatment. In this context, NMR-based serum metabolomic profiling has been explored in this proof-of-principle study for the first time to determine characteristic metabolites that could be potentially helpful for diagnosis and prognosis of TA. Serum metabolic profiling of TA patients (n = 29) and healthy controls (n = 30) was performed using 1D (1)H NMR spectroscopy, and possible biomarker metabolites were identified. Using projection to least-squares discriminant analysis, we could distinguish TA patients from healthy controls. Compared to healthy controls, TA patients had (a) increased serum levels of choline metabolites, LDL cholesterol, N-acetyl glycoproteins (NAGs), and glucose and (b) decreased serum levels of lactate, lipids, HDL cholesterol, and glucogenic amino acids. The results of this study are preliminary and need to be confirmed in a prospective study.

A metabonomic study of adjuvant-induced arthritis in rats using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry

Rheumatoid arthritis (RA) is a chronic systemic inflammatory and autoimmune disease accompanied by the destruction and deformities of joints. Adjuvant-induced arthritis (AIA) is one of the excellent animal models of RA used to understand disease pathogenesis and screen potential drugs. In this paper, a urinary metabonomics method based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) has been established to investigate the disease progression of AIA and find potential biomarkers of secondary inflammation in AIA rats. 24 potential biomarkers were identified, including xanthurenic acid, kynurenic acid, 4-pyridoxic acid, and phenylalanine, which revealed that tryptophan metabolism, phenylalanine metabolism, gut microbiota metabolism and energy metabolism were disturbed in AIA rats. These potential biomarkers and their corresponding pathways are helpful to further understand the mechanisms of AIA and pathogenesis of RA. This study demonstrates that metabonomics based on UPLC-Q-TOF-MS is a powerful methodology to analyze the underlying disease pathogenesis.

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.

Systemic lupus erythematosus diagnostics in the 'omics' era

Systemic lupus erythematosus is a complex autoimmune disease affecting multiple organ systems. Currently, diagnosis relies upon meeting at least four out of eleven criteria outlined by the ACR. The scientific community actively pursues discovery of novel diagnostics in the hope of better identifying susceptible individuals in early stages of disease. Comprehensive studies have been conducted at multiple biological levels including: DNA (or genomics), mRNA (or transcriptomics), protein (or proteomics) and metabolites (or metabolomics). The 'omics' platforms allow us to re-examine systemic lupus erythematosus at a greater degree of molecular resolution. More importantly, one is hopeful that these 'omics' platforms may yield newer biomarkers for systemic lupus erythematosus that can help clinicians track the disease course with greater sensitivity and specificity.

NMR-based metabolomics coupled with pattern recognition methods in biomarker discovery and disease diagnosis

Molecular biomarkers could detect biochemical changes associated with disease processes. The key metabolites have become an important part for improving the diagnosis, prognosis, and therapy of diseases. Because of the chemical diversity and dynamic concentration range, the analysis of metabolites remains a challenge. Assessment of fluctuations on the levels of endogenous metabolites by advanced NMR spectroscopy technique combined with multivariate statistics, the so-called metabolomics approach, has proved to be exquisitely valuable in human disease diagnosis. Because of its ability to detect a large number of metabolites in intact biological samples with isotope labeling of metabolites using nuclei such as H, C, N, and P, NMR has emerged as one of the most powerful analytical techniques in metabolomics and has dramatically improved the ability to identify low concentration metabolites and trace important metabolic pathways. Multivariate statistical methods or pattern recognition programs have been developed to handle the acquired data and to search for the discriminating features from biosample sets. Furthermore, the combination of NMR with pattern recognition methods has proven highly effective at identifying unknown metabolites that correlate with changes in genotype or phenotype. The research and clinical results achieved through NMR investigations during the first 13 years of the 21st century illustrate areas where this technology can be best translated into clinical practice. In this review, we will present several special examples of a successful application of NMR for biomarker discovery, implications for disease diagnosis, prognosis, and therapy evaluation, and discuss possible future improvements.

Serum metabolic signatures of four types of human arthritis

Similar symptoms of the different types of arthritis have continued to confound the clinical diagnosis and represent a clinical dilemma making treatment choices with a more personalized or generalized approach. Here we report a mass spectrometry-based metabolic phenotyping study to identify the global metabolic defects associated with arthritis as well as metabolic signatures of four major types of arthritis--rheumatoid arthritis (n = 27), osteoarthritis (n = 27), ankylosing spondylitis (n = 27), and gout (n = 33)--compared with healthy control subjects (n = 60). A total of 196 metabolites were identified from serum samples using a combined gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF MS) and ultraperformance liquid chromatography quadrupole-time-of-flight mass spectrometry (UPLC-QTOF MS). A global metabolic profile is identified from all arthritic patients, suggesting that there are common metabolic defects resulting from joint inflammation and lesion. Meanwhile, differentially expressed serum metabolites are identified constituting an unique metabolic signature of each type of arthritis that can be used as biomarkers for diagnosis and patient stratification. The results highlight the applicability of metabonomic phenotyping as a novel diagnostic tool for arthritis complementary to existing clinical modalities.

Metabolite analysis distinguishes between mice with epidermolysis bullosa acquisita and healthy mice

Background

Epidermolysis bullosa acquisita (EBA) is a rare skin blistering disease with a prevalence of 0.2/ million people. EBA is characterized by autoantibodies against type VII collagen. Type VII collagen builds anchoring fibrils that are essential for the dermal-epidermal junction. The pathogenic relevance of antibodies against type VII collagen subdomains has been demonstrated both in vitro and in vivo. Despite the multitude of clinical and immunological data, no information on metabolic changes exists.

Methods

We used an animal model of EBA to obtain insights into metabolomic changes during EBA. Sera from mice with immunization-induced EBA and control mice were obtained and metabolites were isolated by filtration. Proton nuclear magnetic resonance (NMR) spectra were recorded and analyzed by principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA) and random forest.

Results

The metabolic pattern of immunized mice and control mice could be clearly distinguished with PCA and PLS-DA. Metabolites that contribute to the discrimination could be identified via random forest. The observed changes in the metabolic pattern of EBA sera, i.e. increased levels of amino acid, point toward an increased energy demand in EBA.

Conclusions

Knowledge about metabolic changes due to EBA could help in future to assess the disease status during treatment. Confirming the metabolic changes in patients needs probably large cohorts.

The applied basic research of systemic lupus erythematosus based on the biological omics

Systemic lupus erythematosus (SLE) is a systemic autoimmune disease characterized by the production of autoantibodies directed against nuclear self-antigens and circulating immune complexes. This results in damages to various organs or systems, including skin, joints, kidneys and the central nervous system. Clinical manifestations of SLE could be diverse, including glomerulonephritis, dermatitis, thrombosis, vasculitis, seizures and arthritis. The complicated pathogenesis and varied clinical symptoms of SLE pose great challenges in the diagnosis and monitoring of this disease. Unfortunately, the etiological factors and pathogenesis of SLE are still not completely understood. It is noteworthy that recent advances in our understanding of the biological omics and emerging technologies have been providing new tools in the analyses of SLE, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics and so on. In this article, we summarize our current knowledge in this field for a better understanding of the pathogenesis, diagnosis and treatment for SLE.

Metabolomic analysis of the plasma of patients with high-altitude pulmonary edema (HAPE) using 1H NMR

Upon rapid ascent to a high altitude, non-acclimatized individuals, although healthy, are highly prone to contracting high-altitude pulmonary edema (HAPE). Early diagnosis is difficult and there is no reliable biomarker available. We used proton ((1)H) NMR metabolomics to profile the altered metabolic patterns of blood plasma from HAPE patients. The plasmas of ten patients with HAPE and ten individuals without HAPE were collected and compared using (1)H NMR spectroscopy. Data were evaluated with several multivariate statistical analyses, including the principal components, the orthogonal partial least-squares discriminant, and the orthogonal signal correction partial least-squares discriminant. Multivariate statistical analyses revealed a significant disparity between subjects with HAPE and those in the control group. Compared to the plasma of the controls, the HAPE patients had significant increases in valine, lysine, leucine, isoleucine, glycerol phosphoryl choline, glycine, glutamine, glutamic acid, creatinine, citrate, and methyl histidine. These were accompanied by decreases in α- and β-glucose, trimethylamine, and the metabolic products of lipids. The data demonstrate that metabolomics may be effective for the diagnosis of HAPE in the future, and can be used for further understanding HAPE pathogenesis.