Metabolomics in rheumatic diseases: The potential of an emerging methodology for improved patient diagnosis, prognosis, and treatment efficacy

Analysis of the volatile organic compounds of epidural analgesia-ameliorated metabolic disorder in pregnant women with gestational diabetes mellitus based on untargeted metabolomics

Gestational diabetes mellitus (GDM) is a metabolic disease with an increasing annual incidence. Our previous observational study found that pregnant women with gestational diabetes had mild cognitive decline, which may be related to methylglyoxal (MGO). This study aimed to investigate whether labor pain aggravates the increase in MGO and explored the protective effect of epidural analgesia on metabolism in pregnant women with GDM based on solid-phase microextraction gas chromatography/mass spectrometry (SPME/GC-MS). Pregnant women with GDM were divided into a natural birth group (ND group, n = 30) and epidural analgesia group (PD group, n = 30). After fasting for ≥ 10 h overnight, venous blood samples were collected pre- and post-delivery to detect MGO, interleukin-6 (IL-6), and 8-epi-prostaglandin F2 alpha (8-iso-PGF2α) by ELISA. Serum samples were analyzed for volatile organic compounds (VOCs) using SPME-GC-MS. MGO, IL-6, and 8-iso-PGF2α levels in the ND group increased significantly post-delivery (P < 0.05) and were significantly higher in this group than the levels in the PD group (P < 0.05). Compared to the PD group, VOCs in the ND group increased significantly post-delivery. Further results indicated that propionic acid may be associated with metabolic disorders in pregnant women with GDM. Epidural analgesia can effectively improve the metabolism and immune function in pregnant women with GDM.

Metabolomics: An Emerging Approach to Understand Pathogenesis and to Assess Diagnosis and Response to Treatment in Spondyloarthritis

Spondyloarthritis (SpA) is a group of rheumatic diseases whose pathogenesis relies on a complex interplay between genetic and environmental factors. Over the last several years, the importance of the alteration of the gut microbiota, known as dysbiosis, and the interaction of bacterial products with host immunity have been highlighted as intriguing key players in SpA development. The recent advent of the so called “-omics” sciences, that include metabolomics, opened the way to a new approach to SpA through a deeper characterisation of the pathogenetic mechanisms behind the disease. In addition, metabolomics can reveal potential new biomarkers to diagnose and monitor SpA patients. The aim of this review is to highlight the most recent advances concerning the application of metabolomics to SpA, in particular focusing attention on Ankylosing Spondylitis and Psoriatic Arthritis.

Metabolomics of Synovial Fluid and Infrapatellar Fat Pad in Patients with Osteoarthritis or Rheumatoid Arthritis

Osteoarthritis (OA) and autoimmune-driven rheumatoid arthritis (RA) are inflammatory joint diseases with complex and insufficiently understood pathogeneses. Our objective was to characterize the metabolic fingerprints of synovial fluid (SF) and its adjacent infrapatellar fat pad (IFP) obtained during the same surgical operation from OA and RA knees. Non-targeted metabolite profiling was performed for 5 non-inflammatory trauma controls, 10 primary OA (pOA) patients, and 10 seropositive RA patients with high-resolution mass spectrometry-based techniques, and metabolites were matched with known metabolite identities. Groupwise differences in metabolic features were analyzed with the univariate Welch’s t-test and the multivariate linear discriminant analysis (LDA) and principal component analysis (PCA). Significant discrimination of metabolite profiles was discovered by LDA for both SF and IFP and by PCA for SF based on diagnosis. In addition to a few drug-derived substances, there were 16 and 13 identified metabolites with significant differences between the diagnoses in SF and IFP, respectively. The pathways downregulated in RA included androgen, bile acid, amino acid, and histamine metabolism, and those upregulated included biotin metabolism in pOA and purine metabolism in RA and pOA. The RA-induced downregulation of androgen and bile acid metabolism was observed for both SF and IFP. The levels of 11 lipid metabolites, mostly glycerophospholipids and fatty acid amides, were also altered by these inflammatory conditions. The identified metabolic pathways could be utilized in the future to deepen our understanding of the pathogeneses of OA and RA and to develop not only biomarkers for their early diagnosis but also therapeutic targets.

Metabolites and metabolic pathways associated with rheumatoid arthritis and systemic lupus erythematosus

Rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are chronic autoimmune diseases that result from the combined influence of genetic and environmental factors that promotes the loss of tolerance to cellular components. The complexity of these diseases converts them into a major challenge at the diagnostic and treatment level. Therefore, it is convenient to implement the use of tools for a better understanding of the physiopathology of these diseases to propose reliable biomarkers. The "omics" disciplines like metabolomics and lipidomics allow to study RA and SLE in a higher degree of detail since they evaluate the metabolites and metabolic pathways involved in disease pathogenesis. This review has compiled the information of metabolomics and lipidomics studies where samples obtained from RA and SLE patients were evaluated to find the metabolites and pathways differences between patients and healthy controls. In both diseases, there is a decrease in several amino acids and oxidative stress-related metabolites like glutathione. These findings may be useful for functional metabolomics studies aiming to reprogram the metabolism in a disease setting to recover normal immune cell homeostasis and function.

Metabolomics in Autoimmune Diseases: Focus on Rheumatoid Arthritis, Systemic Lupus Erythematous, and Multiple Sclerosis

The metabolomics approach represents the last downstream phenotype and is widely used in clinical studies and drug discovery. In this paper, we outline recent advances in the metabolomics research of autoimmune diseases (ADs) such as rheumatoid arthritis (RA), multiple sclerosis (MuS), and systemic lupus erythematosus (SLE). The newly discovered biomarkers and the metabolic mechanism studies for these ADs are described here. In addition, studies elucidating the metabolic mechanisms underlying these ADs are presented. Metabolomics has the potential to contribute to pharmacotherapy personalization; thus, we summarize the biomarker studies performed to predict the personalization of medicine and drug response.

Role of Environment in Pediatric Rheumatic Diseases

Studying environmental risk factors for pediatric rheumatic diseases (PRD) is important because the identification of these factors may lead to strategies to prevent disease, and to new insights into pathogenesis and therapeutic targets. Compared with other chronic diseases, there are few environmental epidemiology studies in PRD. Although strong risk factors common to all PRDs have not been identified, some exposures including infection, smoke exposure, and ultraviolet radiation have been associated with several of them. High-technology studies, especially of microbiomics and metabolomics, are increasing and will likely lead to new understandings of the complex interplay between environment, genetics, and disease.

NMR-Based Metabolomics Revealed the Underlying Inflammatory Pathology in Reactive Arthritis Synovial Joints

Reactive arthritis (ReA) is an aseptic synovitis condition that often develops 2-4 weeks after a distant (extra-articular) infection with Chlamydia, Salmonella, Shigella, Campylobacter, and Yersinia species. The metabolic changes in the synovial fluid (SF) may serve as indicative markers to both improve the diagnostic accuracy and understand the underlying inflammatory pathology of ReA. With this aim, the metabolic profiles of SF collected from ReA (n = 58) and non-ReA, i.e., rheumatoid arthritis (RA, n = 21) and osteoarthritis (OA, n = 20) patients, respectively, were measured using NMR spectroscopy and compared using orthogonal partial least-squares discriminant analysis (OPLS-DA). The discriminatory metabolic features were further evaluated for their diagnostic potential using the receiver operating characteristic (ROC) curve analysis. Compared to RA, two (alanine and carnitine), and compared to OA, six (NAG, glutamate, glycerol, isoleucine, alanine, and glucose) metabolic features were identified as diagnostic biomarkers. We further demonstrated the impact of ReA synovitis condition on the serum metabolic profiles through performing a correlation analysis. The Pearson rank coefficient (r) was estimated for 38 metabolites (profiled in both SF and serum samples obtained in pair from ReA patients) and was found significantly positive for 71% of the metabolites (r ranging from 0.17 to 0.87).

Sub-Cellular Metabolomics Contributes Mitochondria-Specific Metabolic Insights to a Mouse Model of Leigh Syndrome

Direct injury of mitochondrial respiratory chain (RC) complex I by Ndufs4 subunit mutations results in complex I deficiency (CID) and a progressive encephalomyopathy, known as Leigh syndrome. While mitochondrial, cytosolic and multi-organelle pathways are known to be involved in the neuromuscular LS pathogenesis, compartment-specific metabolomics has, to date, not been applied to murine models of CID. We thus hypothesized that sub-cellular metabolomics would be able to contribute organelle-specific insights to known Ndufs4 metabolic perturbations. To that end, whole brains and skeletal muscle from late-stage Ndufs4 mice and age/sex-matched controls were harvested for mitochondrial and cytosolic isolation. Untargeted ¹H-NMR and semi-targeted LC-MS/MS metabolomics was applied to the resulting cell fractions, whereafter important variables (VIPs) were selected by univariate statistics. A predominant increase in multiple targeted amino acids was observed in whole-brain samples, with a more prominent effect at the mitochondrial level. Similar pathways were implicated in the muscle tissue, showing a greater depletion of core metabolites with a compartment-specific distribution, however. The altered metabolites expectedly implicate altered redox homeostasis, alternate RC fueling, one-carbon metabolism, urea cycling and dysregulated proteostasis to different degrees in the analyzed tissues. A first application of EDTA-chelated magnesium and calcium measurement by NMR also revealed tissue- and compartment-specific alterations, implicating stress response-related calcium redistribution between neural cell compartments, as well as whole-cell muscle magnesium depletion. Altogether, these results confirm the ability of compartment-specific metabolomics to capture known alterations related to Ndufs4 KO and CID while proving its worth in elucidating metabolic compartmentalization in said pathways that went undetected in the diluted whole-cell samples previously studied.

Metabolomic Biomarker Candidates for Skeletal Muscle Loss in the Collagen-Induced Arthritis (CIA) Model

There is no consensus for diagnosis or treatment of RA muscle loss. We aimed to investigate metabolites in arthritic mice urine as biomarkers of muscle loss. DBA1/J mice comprised collagen-induced arthritis (CIA) and control (CO) groups. Urine samples were collected at 0, 18, 35, 45, 55, and 65 days of disease and subjected to nuclear magnetic resonance spectroscopy. Metabolites were identified using Chenomx and Birmingham Metabolite libraries. The statistical model used principal component analysis, partial least-squares discriminant analysis, and partial least-squares regression analysis. Linear regression and Fisher's exact test via the MetaboAnalyst website were performed (VIP-score). Nearly 100 identified metabolites had CIA vs. CO and disease time-dependent differences (p < 0.05). Twenty-eight metabolites were muscle-associated: carnosine (VIPs 2.8 × 10²) and succinyl acetone (VIPs 1.0 × 10) showed high importance in CIA vs. CO models at day 65; CIA pair analysis showed histidine (VIPs 1.2 × 10²) days 55 vs. 65, histamine (VIPs 1.1 × 10²) days 55 vs. 65, and L-methionine (VIPs 1.1 × 10²) days 0 vs. 18. Carnosine was fatigue- (0.039) related, creatine was food intake- (-0.177) and body weight- (-0.039) related, and both metabolites were clinical score- (0.093; 0.050) and paw edema- (0.125; 0.026) related. Therefore, muscle metabolic alterations were detected in arthritic mice urine, enabling further validation in RA patient's urine, targeting prognosis, diagnosis, and monitoring of RA-mediated muscle loss.

Metabolomic Biomarker Candidates for Skeletal Muscle Loss in the Collagen-Induced Arthritis (CIA) Model

There is no consensus for diagnosis or treatment of RA muscle loss. We aimed to investigate metabolites in arthritic mice urine as biomarkers of muscle loss. DBA1/J mice comprised collagen-induced arthritis (CIA) and control (CO) groups. Urine samples were collected at 0, 18, 35, 45, 55, and 65 days of disease and subjected to nuclear magnetic resonance spectroscopy. Metabolites were identified using Chenomx and Birmingham Metabolite libraries. The statistical model used principal component analysis, partial least-squares discriminant analysis, and partial least-squares regression analysis. Linear regression and Fisher’s exact test via the MetaboAnalyst website were performed (VIP-score). Nearly 100 identified metabolites had CIA vs. CO and disease time-dependent differences (p < 0.05). Twenty-eight metabolites were muscle-associated: carnosine (VIPs 2.8 × 102) and succinyl acetone (VIPs 1.0 × 10) showed high importance in CIA vs. CO models at day 65; CIA pair analysis showed histidine (VIPs 1.2 × 102) days 55 vs. 65, histamine (VIPs 1.1 × 102) days 55 vs. 65, and L-methionine (VIPs 1.1 × 102) days 0 vs. 18. Carnosine was fatigue- (0.039) related, creatine was food intake- (−0.177) and body weight- (−0.039) related, and both metabolites were clinical score- (0.093; 0.050) and paw edema- (0.125; 0.026) related. Therefore, muscle metabolic alterations were detected in arthritic mice urine, enabling further validation in RA patient’s urine, targeting prognosis, diagnosis, and monitoring of RA-mediated muscle loss.

A review of applications of metabolomics in osteoarthritis

Osteoarthritis (OA) represents the most prevalent and disabling arthritis worldwide due to its heterogeneous and progressive articular degradation. However, effective and timely diagnosis and fundamental treatment for this disorder are lacking. Metabolomics, a growing field in life science research in recent years, has the potential to detect many metabolites and thus explains the underlying pathophysiological processes. Hence, new specific metabolic markers and related metabolic pathways can be identified for OA. In this review, we aimed to provide an overview of studies related to the metabolomics of OA in animal models and humans to describe the metabolic changes and related pathways for OA. The present metabolomics studies reveal that the pathogenesis of OA may be significantly related to perturbations of amino acid metabolism. These altered amino acids (e.g., branched-chain amino acids, arginine, and alanine), as well as phospholipids, were identified as potential biomarkers to distinguish patients with OA from healthy individuals.

Non-target metabolomic analysis reveals the therapeutic effect of Saposhnikovia divaricata decoction on collagen-induced arthritis rats

ETHNOPHARMACOLOGICAL RELEVANCE

Saposhnikovia divaricata (SD), a Chinese crude drug, has long been recognized for therapeutic effect to rheumatoid arthritis (RA). At present, the mechanisms of SD treatment in RA have not been fully understood especially on the perspective of metabolomics.

AIM OF THE STUDY

To study the pharmacodynamic effects of Saposhnikovia divaricata decoction on CIA rats, and explore the therapeutic mechanism by metabolomics methods.

MATERIALS AND METHODS

Wistar rats were randomly divided into normal group, CIA model group, dexamethasone group and SD decoction groups (10 g crude drug/kg, 5 g crude drug/kg and 2.5 g crude drug/kg of SDD). Body weight, arthritis scores, serum cytokine levels and histopathological parameters of rats were assessed. A metabolomics method based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOFMS) was established to collect the metabolic profiles of rats and explore the metabolic changes that occurred after SDD treatment.

RESULTS

SDD showed its protective effect on the affected joints, especially in the middle dosage group of SDD. Eighteen and 13 potential biomarkers for the SDD treatment of CIA rats were identified in the plasma and urine, respectively. SDD could regulate the disturbed metabolic pathways including tryptophan metabolism, glycerophospholipid catabolism, primary bile acid biosynthesis and fatty acid metabolism.

CONCLUSIONS

In summary, SDD treatment could effectively alleviate symptoms of RA and regulate metabolic disorders in CIA rats.

Fatty Acids and Oxylipins in Osteoarthritis and Rheumatoid Arthritis-a Complex Field with Significant Potential for Future Treatments

Purpose of Review

Osteoarthritis (OA) and rheumatoid arthritis (RA) are characterized by abnormal lipid metabolism manifested as altered fatty acid (FA) profiles of synovial fluid and tissues and in the way dietary FA supplements can influence the symptoms of especially RA. In addition to classic eicosanoids, the potential roles of polyunsaturated FA (PUFA)-derived specialized pro-resolving lipid mediators (SPM) have become the focus of intensive research. Here, we summarize the current state of knowledge of the roles of FA and oxylipins in the degradation or protection of synovial joints.

Recent Findings

There exists discordance between the large body of literature from cell culture and animal experiments on the adverse and beneficial effects of individual FA and the lack of effective treatments for joint destruction in OA and RA patients. Saturated 16:0 and 18:0 induce mostly deleterious effects, while long-chain n-3 PUFA, especially 20:5n-3, have positive influence on joint health. The situation can be more complex for n-6 PUFA, such as 18:2n-6, 20:4n-6, and its derivative prostaglandin E₂, with a combination of potentially adverse and beneficial effects. SPM analogs have future potential as analgesics for arthritic pain.

Summary

Alterations in FA profiles and their potential implications in SPM production may affect joint lubrication, synovial inflammation, pannus formation, as well as cartilage and bone degradation and contribute to the pathogeneses of inflammatory joint diseases. Further research directions include high-quality randomized controlled trials on dietary FA supplements and investigations on the significance of lipid composition of microvesicle membrane and cargo in joint diseases.

Metabolic Reprogramming and Inflammatory Response Induced by D-Lactate in Bovine Fibroblast-Like Synoviocytes Depends on HIF-1 Activity

Acute ruminal acidosis (ARA) occurs after an excessive intake of rapidly fermentable carbohydrates and is characterized by the overproduction of D-lactate in the rumen that reaches the bloodstream. Lameness presentation, one of the primary consequences of ARA in cattle, is associated with the occurrence of laminitis and aseptic polysynovitis. Fibroblast-like synoviocytes (FLS) are predominant cells of synovia and play a key role in the pathophysiology of joint diseases, thus increasing the chances of the release of pro-inflammatory cytokines. Increased D-lactate levels and disturbances in the metabolism of carbohydrates, pyruvates, and amino acids are observed in the synovial fluid of heifers with ARA-related polysynovitis prior to neutrophil infiltration, suggesting an early involvement of metabolic disturbances in joint inflammation. We hypothesized that D-lactate induces metabolic reprogramming, along with an inflammatory response, in bovine exposed FLS. Gas chromatography-mass spectrometry (GC-MS)-based metabolomics revealed that D-lactate disrupts the metabolism of bovine FLS, mainly enhancing glycolysis and gluconeogenesis, pyruvate metabolism, and galactose metabolism. The reverse-transcription quantitative PCR (RT-qPCR) analysis revealed an increased expression of metabolic-related genes, including hypoxia-inducible factor 1 (HIF-1)α, glucose transporter 1 (Glut-1), L-lactate dehydrogenase subunit A (L-LDHA), and pyruvate dehydrogenase kinase 1 (PDK-1). Along with metabolic disturbances, D-lactate also induced an overexpression and the secretion of IL-6. Furthermore, the inhibition of HIF-1, PI3K/Akt, and NF-κB reduced the expression of IL-6 and metabolic-related genes. The results of this study reveal a potential role for D-lactate in bFLS metabolic reprogramming and support a close relationship between inflammation and metabolism in cattle.

Intervening upregulated SLC7A5 could mitigate inflammatory mediator by mTOR-P70S6K signal in rheumatoid arthritis synoviocytes

Objective

The disruption of metabolic events and changes to nutrient and oxygen availability due to sustained inflammation in RA increases the demand of bioenergetic and biosynthetic processes within the damaged tissue. The current study aimed to understand the molecular mechanisms of SLC7A5 (amino acid transporter) in synoviocytes of RA patients.

Methods

Synovial tissues were obtained from OA and RA patients. Fibroblast-like synoviocytes (FLS) were isolated, and SLC7A5 expression was examined by using RT-qPCR, immunofluorescence, and Western blotting. RNAi and antibody blocking treatments were used to knockdown SLC7A5 expression or to block its transporter activities. mTOR activity assay and MMP expression levels were monitored in RA FLS under amino acid deprivation or nutrient-rich conditions.

Results

RA FLS displayed significantly upregulated expression of SLC7A5 compared to OA FLS. Cytokine IL-1β was found to play a crucial role in upregulating SLC7A5 expression via the NF-κB pathway. Intervening SLC7A5 expression with RNAi or blocking its function by monoclonal antibody ameliorated MMP3 and MMP13 protein expression. Conversely, upregulation of SLC7A5 or tryptophan supplementation enhanced mTOR-P70S6K signals which promoted the protein translation of MMP3 and MMP13 in RA FLS.

Conclusion

Activated NF-κB pathway upregulates SLC7A5, which enhances the mTOR-P70S6K activity and MMP3 and MMP13 expression in RA FLS.

Changes in Plasma Itaconate Elevation in Early Rheumatoid Arthritis Patients Elucidates Disease Activity Associated Macrophage Activation

Changes in the plasma metabolic profile were characterised in newly diagnosed rheumatoid arthritis (RA) patients upon commencement of conventional disease-modifying anti-rheumatic drug (cDMARD) therapy. Plasma samples collected in an early RA randomised strategy study (NCT00920478) that compared clinical (DAS) disease activity assessment with musculoskeletal ultrasound assessment (MSUS) to drive treatment decisions were subjected to untargeted metabolomic analysis. Metabolic profiles were collected at pre- and three months post-commencement of nonbiologic cDMARD. Metabolites that changed in association with changes in the DAS44 score were identified at the three-month timepoint. A total of nine metabolites exhibited a clear correlation with a reduction in DAS44 score following cDMARD commencement, particularly itaconate, its derived anhydride and a derivative of itaconate CoA. Increasing itaconate correlated with improved DAS44 score and decreasing levels of C-reactive protein (CRP). cDMARD treatment effects invoke consistent changes in plasma detectable metabolites, that in turn implicate clinical disease activity with macrophages. Such changes inform RA pathogenesis and reveal for the first time a link between itaconate production and resolution of inflammatory disease in humans. Quantitative metabolic biomarker-based tests of clinical change in state are feasible and should be developed around the itaconate pathway.

Metabolomics in the development and progression of rheumatoid arthritis: A systematic review

OBJECTIVE

A systematic review and analysis of data from several rheumatoid arthritis metabolomics studies attempts to determine which metabolites can be used as potential biomarkers for the diagnosis of rheumatoid arthritis and to explore the pathogenesis of rheumatoid arthritis.

METHODS

We searched all the subject-related documents published by EMBASE, PubMed, Web of Science, and Cochrane Library from the database to the September 2019 publication. Two researchers independently screened the literature and extracted the data. QUADOMICS tool was used to assess the quality of studies included in this systematic review.

RESULTS

A total of 10 studies met the inclusion criteria of systematic review, including 502 patients with rheumatoid arthritis and 373 healthy people. Among them, the biological samples utilised for metabolomic analysis include: serum (n=8), urine (n=1) and synovial fluid (n=1). Some metabolites play an important role in rheumatoid arthritis: glucose, lactic acid, citric acid, leucine, methionine, isoleucine, valine, phenylalanine, threonine, serine, proline, glutamate, histidine, alanine, cholesterol, glycerol, and ribose.

CONCLUSIONS

Metabolomics provides important new opportunities for further research in rheumatoid arthritis and is expected to elucidate the pathogenesis of rheumatoid arthritis that has not been fully understood before.

1H-NMR based metabolomic profiling of cord blood in gestational hypothyroidism

Background

Gestational hypothyroidism (GHT) is a common pregnancy-related thyroid disfunction. The adverse outcomes by GHT has been increasingly recognized, leading to more public awareness of the disease. However, comprehensive understanding of the prognosis of GHT has not yet achieved. Metabolomics is a powerful tool in evaluation of disease outcomes, and cord blood represents an excellent candidate for the investigation of gestational outcomes.

Methods

In the present study, we performed 1H-NMR based metabolomics on cord blood of 18 pregnant women with GHT and 18 non hypothyroidism (NHT) control.

Results

The metabolomic profile of GHT was separated with the NHT control. A total of 8 metabolites with altered abundances were observed, among which Creatinine and O-Phosphocholine were elevated and the others were downregulated in GHT. Spearman rank correlation suggested that the eight differential metabolites were correlated with the GHT related thyroid hormones. Pathway analysis of the differential metabolites indicated that two metabolic pathways were significantly altered in GHT (adjusted P<0.05), including tyrosine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis. Enrichment analysis of the differential metabolites against disease-associated metabolite sets suggested that GHT was associated with disease risks of non-insulin dependent diabetes mellitus, isovaleric acidemia, and methylmalonic aciduria.

Conclusions

The results of this study revealed GHT associated metabolic changes in cord blood, providing insights into the metabolic intermediates between GHT and its related disease risks.

Biomarkers identified by serum metabolomic analysis to predict biologic treatment response in rheumatoid arthritis patients

OBJECTIVES

Biologic treatment has recently revolutionized the management of RA. Despite this success, ∼30-40% of the patients undergoing biologic treatment respond insufficiently. The aim of this study was to identify several specific reliable metabolites for predicting the response of RA patients to TNF-α inhibitors (TNFi) and abatacept (ABT), using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS).

METHODS

We collected serum from RA patients with moderate or high disease activity prior to biologic treatment, and obtained the serum metabolomic profiles of these samples using CE-TOFMS. The patients' response was determined 12 weeks after starting biologic treatment, according to the EULAR response criteria. We compared the metabolites between the response and non-response patient groups and analysed their discriminative ability.

RESULTS

Among 43 total patients, 14 of 26 patients in the TNFi group and 6 of 17 patients in the ABT group responded to the biologic treatment. Of the metabolites separated by CE-TOFMS, 196 were identified as known substances. Using an orthogonal partial least-squares discriminant analysis, we identified five metabolites as potential predictors of TNFi responders and three as predictors of ABT responders. Receiver operating characteristic analyses for multiple biomarkers revealed an area under the curve (AUC) of 0.941, with a sensitivity of 85.7% and specificity of 100% for TNFi, and an AUC of 0.985, with a sensitivity of 100% and specificity of 90.9% for ABT.

CONCLUSION

By metabolomic analysis, we identified serum biomarkers that have a high ability to predict the response of RA patients to TNFi or ABT treatment.

Untargeted metabolomics reveals novel serum biomarker of renal damage in rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic progressive disease, it often involves kidney, lung, heart, and other systems.Renal damage is quite common in RA. Exploring of biomarkers of renal damage in the course of RA progression is of significant importance for disease diagnosis and treatment. We use type II Collagen-Induced Arthritis(CIA) Model. Serums were collected at the 4th, 6th, 8th, and 10th week after the first immunization. An untargeted metabonomic strategy based on UPLC-Q/TOF/MS with support vector machine(SVM) was developed to discover the biomarkers in the rats' serum samples between the RA stage(4-6 weeks in RA model, at which time the kidneys are not affected) and renal damage in RA stage(8-10 weeks in RA model, and the kidneys are affected). Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were used to analyze the metabolic profiles of rat serum. The support vector machine (SVM) method was used to screen the specific markers of renal damage in RA. Following multivariate statistical and integration analysis, 5 specific markers of renal damage in RA were screened and found. After the analysis of these metabolites, pentose and glucuronate interconversions are closely related to the pathogenesis of RA renal damage. The present study first use untargeted dmetabonomics combined with the pathological features in the different phases of CIA model rats. This will provide a basis for the choice of treatment drugs for patients with RA who may be complicated by renal damage.

Metabolomic analysis of synovial fluid from Thoroughbred racehorses diagnosed with palmar osteochondral disease using magnetic resonance imaging

BACKGROUND

Palmar osteochondral disease (POD) is a common cause of lameness in competition horses. Magnetic resonance imaging (MRI) is the most sensitive diagnostic modality currently available, however it may not be financially or logistically practical for routine screening of POD. There is increasing interest in the use of metabolomics for diagnosis prior to progression to irreversible damage.

OBJECTIVES

To determine metabolite levels in synovial fluid (SF) of horses with a clinical diagnosis of POD based on diagnostic analgesia and MRI, with the hypothesis that metabolomic profiles differ between diseased and healthy joints.

STUDY DESIGN

Prospective clinical study.

METHODS

Synovial fluid was collected from metacarpo/tarsophalangeal joints (MC/TPJ) of 29 horses (n = 51 joints), including 14 controls (n = 26) and 15 cases (n = 25), the latter with lameness localised to the MC/TPJ and MR changes consistent with POD (n = 23). Spectra were produced using ¹ H-nuclear magnetic resonance (NMR) spectroscopy and analysed.

RESULTS

Twenty-five metabolites were recognised associated with various biosynthetic and degradation pathways. The metabolite abundances within the controls demonstrated increased variability compared with the clinical group. The low level of variance between the spectra of the two groups was explained by five principal components. Cross-validation of the cohort demonstrated modest separation of predictive power (R²  = 0.67; Q²  = 0.34). Although statistical significance was not achieved, the most influential metabolites were glucose and lactate.

MAIN LIMITATIONS

The modest sample size and variation in signalment, background and presenting condition of the controls may have impacted the discriminative power of the constructed models. The lack of matched controls, differences in time of fluid collection and freezing times may have also reduced accuracy when representing metabolite profiles.

CONCLUSIONS

This study identified and quantified metabolites present in MC/TPJ SF of clinical cases with POD.

The association of tryptophan and phenylalanine are associated with arsenic-induced skin lesions in a Chinese population chronically exposed to arsenic via drinking water: a case-control study

OBJECTIVES

We investigated the association of specific serum amino acids (AAs) with the odds of arsenic-induced skin lesions (AISL) and their ability to distinguish patients with AISL from people chronically exposed to arsenic.

DESIGN

Case-control study.

SETTING

Three arsenic-exposed villages in Wuyuan County, Hetao Plain, Inner Mongolia, China were evaluated.

PARTICIPANTS

Among the 450 residents aged 18-79 years, who were chronically exposed to arsenic via drinking water, 56 were diagnosed as having AISL (defined as cases). Another 56 participants without AISL, matched by gender and age (±1 year) from the same population, were examined as controls.

MAIN OUTCOME MEASURES AND METHODS

AA levels were determined by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based metabolomics analysis. Potential confounding variables were identified via a standardised questionnaire and clinical examination. Multivariable conditional logistic regression model and receiver operating characteristic curve analyses were performed to investigate the relationship between specific AAs and AISL.

RESULTS

Tryptophan and phenylalanine levels were negatively associated with AISL (p<0.05). Compared with that in the first quartile, the adjusted OR of AISL in the second, third and fourth quartiles were decreased by 44%, 88% and 79% for tryptophan and 30%, 80% and 80% for phenylalanine, respectively. The combination of these two higher-level AAs showed the lowest OR for AISL (OR=0.08; 95% CI 0.02 to 0.25; p<0.001). Furthermore, both AAs showed a moderate ability to distinguish patients with AISL from the control, with the area under the curve (AUC; 95% CI) as 0.67 (0.57 to 0.77) for tryptophan and 0.70 (0.60 to 0.80) for phenylalanine (p<0.05). The combined pattern with AUC (95% CI) was 0.72 (0.62 to 0.81), showing a sensitivity of 76.79% and specificity of 58.93% (p<0.001).

CONCLUSIONS

Specific AAs may be linked to AISL and play important roles in early AISL identification.

TRIAL REGISTRATION NUMBER

NCT02235948.

Application of Metabolomics to Osteoarthritis: from Basic Science to the Clinical Approach

PURPOSE OF THE REVIEW

Osteoarthritis (OA) is a multifactorial and progressive disease affecting whole synovial joint. The extract pathogenic mechanisms and diagnostic biomarkers of OA remain unclear. In this article, we review the studies related to metabolomics of OA, discuss the biomarkers as a tool for early OA diagnosis. Furthermore, we examine the major studies on the application of metabolomics methodology in the complex context of OA and create a bridge from findings in basic science to their clinical utility.

RECENT FINDINGS

Recently, the tissue metabolomics signature permits a view into transitional phases between the healthy and OA joint. Both nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry-based metabolomics approaches have been used to interrogate the metabolic alterations that may indicate the complex progression of OA. Specifically, studies on alterations pertaining to lipids, glucose, and amino acid metabolism have aided in the understanding of the complex pathogenesis of OA. The discovery of identified metabolites could be important for diagnosis and staging of OA, as well as for the assessment of efficacy of new drugs.

Metabolic disturbances in synovial fluid are involved in the onset of synovitis in heifers with acute ruminal acidosis

Acute ruminal acidosis (ARA) is the result of increased intake of highly fermentable carbohydrates, which frequently occurs in dairy cattle and is associated with aseptic polysynovitis. To characterise the metabolic changes in the joints of animals with ARA, we performed an untargeted gas chromatography-mass spectrometry (GC-MS)-based metabolomic analysis of synovial fluid. Seven heifers were challenged with an intraruminal oligofructose overload (13 g/kg of body weight [BW]) dissolved in water. Synovial fluid samples were collected at 0, 9 and 24 h post-overload. Metabolome analysis revealed the presence of 67 metabolites. At 9 h post-overload, glyceric acid, cellobiose, fructose and lactic acid were all increased, whereas at 24 h, sorbitol, lactic acid and fructose levels were all increased >10-fold. At 24 h, citric acid and threonine levels were significantly reduced. We detected increased L- and D-lactate, and the presence of interleukin-6 (IL-6) in synovial fluid. Furthermore, using bovine fibroblast-like synoviocytes, we observed that D-lactate induces IL-6 synthesis. Our results suggest that ARA produces severe metabolomic changes in synovial fluid, including disturbances in starch and sucrose metabolism, and increased lactate levels. These changes were observed prior to the appearance of synovitis, suggesting a potential role in the onset of polysynovitis.

Osteoarthritic Synovial Fluid Modulates Cell Phenotype and Metabolic Behavior In Vitro

Synovial fluid holds a population of mesenchymal stem cells (MSC) that could be used for clinical treatment. Our goal was to characterize the inflammatory and metabolomic profile of the synovial fluid from osteoarthritic patients and to identify its modulatory effect on synovial fluid cells. Synovial fluid was collected from non-OA and OA patients, which was centrifuged to isolate cells. Cells were cultured for 21 days, characterized with specific markers for MSC, and exposed to a specific cocktail to induce chondrogenic, osteogenic, and adipogenic differentiation. Then, we performed a MTT assay exposing SF cells from non-OA and OA patients to a medium containing non-OA and OA synovial fluid. Synovial fluid from non-OA and OA patients was submitted to ELISA to evaluate BMP-2, BMP-4, IL-6, IL-10, TNF-α, and TGF-β1 concentrations and to a metabolomic evaluation using ¹H-NMR. Synovial fluid cells presented spindle-shaped morphology in vitro. Samples from OA patients formed a higher number of colonies than the ones from non-OA patients. After 21 days, the colony-forming cells from OA patients differentiated into the three mesenchymal cell lineages, under the appropriated induction protocols. Synovial fluid cells increased its metabolic activity after being exposed to the OA synovial fluid. ELISA assay showed that OA synovial fluid samples presented higher concentration of IL-10 and TGF-β1 than the non-OA, while the NMR showed that OA synovial fluid presents higher concentrations of glucose and glycerol. In conclusion, SFC activity is modulated by OA synovial fluid, which presents higher concentration of IL-10, TGF-β, glycerol, and glucose.

Urinary Metabolomics Study of Patients with Gout Using Gas Chromatography-Mass Spectrometry

Objectives

Gout is a common type of inflammatory arthritis. The aim of this study was to detect urinary metabolic changes in gout patients which may contribute to understanding the pathological mechanism of gout and discovering potential metabolite markers.

Methods

Urine samples from 35 gout patients and 29 healthy volunteers were analyzed by gas chromatography-mass spectrometry (GC-MS). Orthogonal partial least-squares discriminant analysis (OPLS-DA) was performed to screen differential metabolites between two groups, and the variable importance for projection (VIP) values and Student's t-test results were combined to define the significant metabolic changes caused by gout. Further, binary logistic regression analysis was performed to establish a model to distinguish gout patients from healthy people, and receiver operating characteristic (ROC) curve was made to evaluate the potential for diagnosis of gout.

Result

A total of 30 characteristic metabolites were significantly different between gout patients and controls, mainly including amino acids, carbohydrates, organic acids, and their derivatives, associated with perturbations in purine nucleotide synthesis, amino acid metabolism, purine metabolism, lipid metabolism, carbohydrate metabolism, and tricarboxylic acid cycle. Binary logistic regression and ROC curve analysis showed the combination of urate and isoxanthopterin can effectively discriminate the gout patients from controls with the area under the curve (AUC) of 0.879.

Conclusion

Thus, the urinary metabolomics study is an efficient tool for a better understanding of the metabolic changes of gout, which may support the clinical diagnosis and pathological mechanism study of gout.

Serum free amino acid levels in rheumatoid arthritis according to therapy and physical disability

BACKGROUND

In presented study the amino acid analysis was performed in serum derived from rheumatoid arthritis patients (RA) according to undertaken therapy and classification of physical disability. The results were compared with previously published data.

METHODS

The levels of 31 free amino acids were determined in 50 serum samples derived from RA subjects and 51 controls. The RA patients were divided into two groups according to the therapy (methotrexate/leflunomide, infliximab/adalimumab/etanercept/tocilizumab, prednisolone/NSAID) and classification of physical disability of the patients. Levels of amino acids were measured by LC-MS/MS. The obtained results were subjected to multivariate statistical tests.

RESULTS

According to the therapy that was being used, threonine differentiated RA patients treated with methotrexate/leflunomide - infliximab/adalimumab/etanercept/tocilizumab (p = 0.00954) and infliximab/adalimumab/etanercept/tocilizumab - prednisolone/NSAID (p = 0.03109), while tryptophan differentiated RA patients treated with methotrexate/leflunomide - infliximab/adalimumab/etanercept/tocilizumab (p = 0.01723). In the functional classification, arginine differentiated RA samples between class III and IV (p = 0.02332), while glycine differentiated them between class I+II and III of the Steinbrocker functional classification (p = 0.03366).

CONCLUSIONS

An analysis of the metabolome profile requires the use of validated bioanalytical methods that are strictly dedicated for this purpose. The obtained results are not accidental (p value less than 0.05), and all of the selected amino acids play an important role in inflammation and immune response. It is suggested that studied amino acids can be considered as a markers for diagnosis of RA and monitoring pharmacotherapy of the disease.

Baseline metabolic profiles of early rheumatoid arthritis patients achieving sustained drug-free remission after initiating treat-to-target tocilizumab, methotrexate, or the combination: insights from systems biology

Background

We previously identified, in newly diagnosed rheumatoid arthritis (RA) patients, networks of co-expressed genes and proteomic biomarkers associated with achieving sustained drug-free remission (sDFR) after treatment with tocilizumab- or methotrexate-based strategies. The aim of this study was to identify, within the same patients, metabolic pathways important for achieving sDFR and to subsequently study the complex interactions between different components of the biological system and how these interactions might affect the therapeutic response in early RA.

Methods

Serum samples were analyzed of 60 patients who participated in the U-Act-Early trial (ClinicalTrials.gov number NCT01034137) and initiated treatment with methotrexate, tocilizumab, or the combination and who were thereafter able to achieve sDFR (n = 37); as controls, patients were selected who never achieved a drug-free status (n = 23). Metabolomic measurements were performed using mass spectrometry on oxidative stress, amine, and oxylipin platforms covering various compounds. Partial least square discriminant analyses (PLSDA) were performed to identify, per strategy arm, relevant metabolites of which the biological pathways were studied. In addition, integrative analyses were performed correlating the previously identified transcripts and proteins with the relevant metabolites.

Results

In the tocilizumab plus methotrexate, tocilizumab, and methotrexate strategy, respectively, 19, 13, and 12 relevant metabolites were found, which were subsequently used for pathway analyses. The most significant pathway in the tocilizumab plus methotrexate strategy was “histidine metabolism” (p < 0.001); in the tocilizumab strategy it was “arachidonic acid metabolism” (p = 0.018); and in the methotrexate strategy it was “arginine and proline metabolism” (p = 0.022). These pathways have treatment-specific drug interactions with metabolites affecting either the signaling of interleukin-6, which is inhibited by tocilizumab, or affecting protein synthesis from amino acids, which is inhibited by methotrexate.

Conclusion

In early RA patients treated-to-target with a tocilizumab- or methotrexate-based strategy, several metabolites were found to be associated with achieving sDFR. In line with our previous observations, by analyzing relevant transcripts and proteins within the same patients, the metabolic profiles were found to be different between the strategy arms. Our metabolic analysis further supports the hypothesis that achieving sDFR is not only dependent on predisposing biomarkers, but also on the specific treatment that has been initiated.

Trial registration

ClinicalTrials.gov, NCT01034137. Registered on January 2010

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1729-2) contains supplementary material, which is available to authorized users.

Serum metabolomic profiling predicts synovial gene expression in rheumatoid arthritis

Background

Metabolomics is an emerging field of biomedical research that may offer a better understanding of the mechanisms of underlying conditions including inflammatory arthritis. Perturbations caused by inflamed synovial tissue can lead to correlated changes in concentrations of certain metabolites in the synovium and thereby function as potential biomarkers in blood. Here, we explore the hypothesis of whether characterization of patients’ metabolomic profiles in blood, utilizing ¹H-nuclear magnetic resonance (NMR), predicts synovial marker profiling in rheumatoid arthritis (RA).

Methods

Nineteen active, seropositive patients with RA, on concomitant methotrexate, were studied. One of the involved joints was a knee or a wrist appropriate for arthroscopy. A Bruker Avance 700 MHz spectrometer was used to acquire NMR spectra of serum samples. Gene expression in synovial tissue obtained by arthroscopy was analyzed by real-time PCR. Data processing and statistical analysis were performed in Python and SPSS.

Results

Analysis of the relationships between each synovial marker-metabolite pair using linear regression and controlling for age and gender revealed significant clustering within the data. We observed an association of serine/glycine/phenylalanine metabolism and aminoacyl-tRNA biosynthesis with lymphoid cell gene signature. Alanine/aspartate/glutamate metabolism and choline-derived metabolites correlated with TNF-α synovial expression. Circulating ketone bodies were associated with gene expression of synovial metalloproteinases. Discriminant analysis identified serum metabolites that classified patients according to their synovial marker levels.

Conclusion

The relationship between serum metabolite profiles and synovial biomarker profiling suggests that NMR may be a promising tool for predicting specific pathogenic pathways in the inflamed synovium of patients with RA.

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1655-3) contains supplementary material, which is available to authorized users.

Gas chromatography-time of flight/mass spectrometry-based metabonomics of changes in the urinary metabolic profile in osteoarthritic rats

The aim of the present study was to explore changes in the urinary metabolic spectrum in rats with knee osteoarthritis, using gas chromatography-time of flight/mass spectrometry (GC-TOF/MS) to determine the metabonomic disease pathogenesis. Sprague-Dawley rats were randomly divided into the control and model groups (n=8/group), and 20 µl of 4% papain and 0.03 M L-cysteine was injected into the right knee on days 1, 3 and 7 to establish the knee osteoarthritis model. Following 14 days, urine was collected over 12 h and cartilage ultrastructural damage was assessed by hematoxylin-eosin staining. GC-TOF/MS, combined with principal component analysis, partial least squares discriminant modeling and orthogonal partial least squares discriminant modeling, was used to analyze the changes in the metabolic spectrum trajectory and to identify potential biomarkers and their related metabolic pathways. Compared with the control group, the synovial cell lining of the knee joint exhibited proliferation, inflammatory cell infiltration and collagen fiber hyperplasia in the knee osteoarthritis group. A total of 23 potential biomarkers were identified, including alanine, α-ketoglutarate, asparagine, maltose and glutamine. Furthermore, metabolomic pathogenesis of osteoarthritis may be related to disorders of amino acid metabolism, energy metabolism, fatty acid metabolism, vitamin B₆ metabolism and nucleic acid metabolism.

Combining a joint health supplement with tibial plateau leveling osteotomy in dogs with cranial cruciate ligament rupture. An exploratory controlled trial

Canine cranial cruciate ligament rupture (CrCLR) is a very common pathology. Surgical stabilization is the first choice treatment, although it does not fully eliminate the increased risk of osteoarthritis. This preliminary study was carried out to explore whether a newly formulated joint health supplement would benefit metabolic, clinical and radiographic changes in dogs with CrCLR surgically treated with tibial plateau leveling osteotomy (TPLO). Besides chondroitin sulfate and glucosamine hydrochloride, the studied supplement contained anti-inflammatory and antioxidant ingredients, the main ones being N-palmitoyl-D-glucosamine (Glupamid®) and quercetin. It was thus intended to target not only chondrodegenerative components of osteoarthritis, but also post-injury inflammatory response and oxidative stress of joint tissues. Thirteen dogs underwent TPLO and were randomly allocated to treatment (n = 6) and control groups (n = 7), the former receiving the joint supplement for 90 days. Lameness and radiographic osteoarthritis changes were scored before (i.e., baseline) and at 30 and 90 days post-surgery. Synovial fluid samples were collected from injured stifles at the same time points. Levels of representative metabolites were measured by proton nuclear magnetic resonance spectroscopy in a blinded fashion. In the metabolomic analysis, special attention was paid to lactate, due to its emerging recognition as a key marker of inflammation. In the last time period (from the 30th to the 90th day), lameness improved by a factor of 2.3 compared to control dogs. No significant difference was observed in the radiographic osteoarthritis score between groups. In the first postoperative month, lactate and creatine levels significantly dropped in treated compared to control dogs. Compared to surgery alone, combining the joint supplement with TPLO resulted in a trend to a better clinical outcome in the later time interval but did not influence osteoarthritis radiographic progression. A significantly better rebalance of joint microenvironment in the early time interval (baseline - 30 days) was shown by metabolomic analysis, thus suggesting that the study supplement could limit ongoing inflammatory responses.

Biomarker Development in Chronic Inflammatory Diseases

Chronic inflammatory diseases, such as inflammatory bowel disease—namely, Crohn’s disease and ulcerative colitis—psoriasis, multiple sclerosis, rheumatoid arthritis, and many others affect millions of people worldwide, causing a high burden of disease, socioeconomic impact, and healthcare cost. These diseases have common features including autoimmune pathogenesis and frequent co morbidity.

The treatment of these chronic inflammatory diseases usually requires long-term immunosuppressive therapies with undesirable side effects. The future of chronic inflammatory disease prevention, detection, and treatment will be greatly influenced by the use of more effective biomarkers with enhanced performance. Given the practical issues of collecting tissue samples in inflammatory diseases, biomarkers derived from body fluids have great potential for optimized patient management through the circumvention of the abovementioned limitations.

In this chapter, peripheral blood, urine, and cerebrospinal fluid biomarkers used in chronic inflammatory conditions are reviewed. In detail, this chapter reviews biomarkers to fore used or emerging to be used in patients with chronic inflammatory conditions. Those include inflammatory bowel diseases, chronic inflammatory conditions of the liver, biliary tract, pancreas, psoriasis, atopic disease, inflammatory skin diseases, rheumatic diseases, demyelination, and also the chronic inflammatory component of various other diseases in general medicine—including diabetes, cardiovascular disease, renal disease, and chronic obstructive pulmonary disease.

Development of personalized medicine is closely linked to biomarkers, which may serve as the basis for diagnosis, drug discovery, and monitoring of diseases.

Metabolomics as a promising tool for early osteoarthritis diagnosis

Osteoarthritis (OA) is the main cause of disability worldwide, due to progressive articular cartilage loss and degeneration. According to recent research, OA is more than just a degenerative disease due to some metabolic components associated to its pathogenesis. However, no biomarker has been identified to detect this disease at early stages or to track its development. Metabolomics is an emerging field and has the potential to detect many metabolites in a single spectrum using high resolution nuclear magnetic resonance (NMR) techniques or mass spectrometry (MS). NMR is a reproducible and reliable non-destructive analytical method. On the other hand, MS has a lower detection limit and is more destructive, but it is more sensitive. NMR and MS are useful for biological fluids, such as urine, blood plasma, serum, or synovial fluid, and have been used for metabolic profiling in dogs, mice, sheep, and humans. Thus, many metabolites have been listed as possibly associated to OA pathogenesis. The goal of this review is to provide an overview of the studies in animal models and humans, regarding the use of metabolomics as a tool for early osteoarthritis diagnosis. The concept of osteoarthritis as a metabolic disease and the importance of detecting a biomarker for its early diagnosis are highlighted. Then, some studies in plasma and synovial tissues are shown, and finally the application of metabolomics in the evaluation of synovial fluid is described.

MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis

Human metabolomics has great potential in disease mechanism understanding, early diagnosis, and therapy. Existing metabolomics studies are often based on profiling patient biofluids and tissue samples and are difficult owing to the challenges of sample collection and data processing. Here, we report an alternative approach and developed a computation-based prediction system, MetabolitePredict, for disease metabolomics biomarker prediction. We applied MetabolitePredict to identify metabolite biomarkers and metabolite targeting therapies for rheumatoid arthritis (RA), a last-lasting complex disease with multiple genetic and environmental factors involved. MetabolitePredict is a de novo prediction system. It first constructs a disease-specific genetic profile using genes and pathways data associated with an input disease. It then constructs genetic profiles for a total of 259,170 chemicals/metabolites using known chemical genetics and human metabolomic data. MetabolitePredict prioritizes metabolites for a given disease based on the genetic profile similarities between disease and metabolites. We evaluated MetabolitePredict using 63 known RA-associated metabolites. MetabolitePredict found 24 of the 63 metabolites (recall: 0.38) and ranked them highly (mean ranking: top 4.13%, median ranking: top 1.10%, P-value: 5.08E-19). MetabolitePredict performed better than an existing metabolite prediction system, PROFANCY, in predicting RA-associated metabolites (PROFANCY: recall: 0.31, mean ranking: 20.91%, median ranking: 16.47%, P-value: 3.78E-7). Short-chain fatty acids (SCFAs), the abundant metabolites of gut microbiota in the fermentation of fiber, ranked highly (butyrate, 0.03%; acetate, 0.05%; propionate, 0.38%). Finally, we established MetabolitePredict's potential in novel metabolite targeting for disease treatment: MetabolitePredict ranked highly three known metabolite inhibitors for RA treatments (methotrexate:0.25%; leflunomide: 0.56%; sulfasalazine: 0.92%). MetabolitePredict is a generalizable disease metabolite prediction system. The only required input to the system is a disease name or a set of disease-associated genes. The web-based MetabolitePredict is available at:http://xulab.

CASE

edu/MetabolitePredict.

Potential metabolomic biomarkers for reliable diagnosis of Behcet's disease using gas chromatography/ time-of-flight-mass spectrometry

OBJECTIVES

Although many diagnostic criteria of Behcet's disease (BD) have been developed and revised by experts, diagnosing BD is still complicated and challenging. No metabolomic studies on serum have been attempted to improve the diagnosis and to identify potential biomarkers of BD. The purposes of this study were to investigate distinctive metabolic changes in serum samples of BD patients and to identify metabolic candidate biomarkers for reliable diagnosis of BD using the metabolomics platform.

METHODS

Metabolomic profiling of 90 serum samples from 45 BD patients and 45 healthy controls (HCs) were performed via gas chromatography with time-of-flight mass spectrometry (GC/TOF-MS) with multivariate statistical analyses.

RESULTS

A total of 104 metabolites were identified from samples. The serum metabolite profiles obtained from GC/TOF-MS analysis can distinguish BD patients from HC group in discovery set. The variation values of the partial least squared-discrimination analysis (PLS-DA) model are R²X of 0.246, R²Y of 0.913 and Q² of 0.852, respectively, indicating strong explanation and prediction capabilities of the model. A panel of five metabolic biomarkers, namely, decanoic acid, fructose, tagatose, linoleic acid and oleic acid were selected and adequately validated as putative biomarkers of BD (sensitivity 100%, specificity 97.1%, area under the curve 0.998) in the discovery set and independent set. The PLS_DA model showed clear discrimination of BD and HC groups by the five metabolic biomarkers in independent set.

CONCLUSIONS

This is the first report on characteristic metabolic profiles and potential metabolite biomarkers in serum for reliable diagnosis of BD using GC/TOF-MS.

Dose-response characteristics of Clematis triterpenoid saponins and clematichinenoside AR in rheumatoid arthritis rats by liquid chromatography/mass spectrometry-based serum and urine metabolomics

Clematidis Radix et Rhizoma is a traditional Chinese medicine widely used for treating arthritic disease. Clematis triterpenoid saponins (TS) and clematichinenoside AR (C-AR) have been considered to be responsible for its antiarthritic effects. However, the underling mechanism is still unclear because of their low bioavailability. To address of this issue, metabolomics tools were performed to determine metabolic variations associated with rheumatoid arthritis (RA) and responses to Clematis TS, C-AR and positive drug (Triptolide, TP) treatments. This metabolomics investigation of RA was conducted in collagen-induced arthritis (CIA) rats. Liquid chromatography/mass spectrometry and multivariate statistical tools were used to identify the alteration of serum and urine metabolites associated with RA and responses to drug treatment. As a result, 45 potential metabolites associated with RA were identified. After treatment, a total of 24 biomarkers were regulated to normal like levels. Among these, PC(18:0/20:4), 9,11-octadecadienoic acid, arachidonic acid, 1-methyladenosine, valine, hippuric acid and pantothenic acid etc, were reversed in Clematis TS and C-AR groups. Tetrahydrocortisol was regulated to normal levels in Clematis TS and TP groups, while 3,7,12-trihydroxycholan-24-oic acid was regulated in C-AR and TP groups. Biomarkers like citric acid, p-cresol glucuronide, creatinine, cortolone were reversed in TP group.

Metabolic Disturbances in Adult-Onset Still's Disease Evaluated Using Liquid Chromatography/Mass Spectrometry-Based Metabolomic Analysis

Objective

Liquid chromatography/mass spectrometry (LC/MS)-based comprehensive analysis of metabolic profiles with metabolomics approach has potential diagnostic and predictive implications. However, no metabolomics data have been reported in adult-onset Still’s disease (AOSD). This study investigated the metabolomic profiles in AOSD patients and examined their association with clinical characteristics and disease outcome.

Methods

Serum metabolite profiles were determined on 32 AOSD patients and 30 healthy controls (HC) using ultra-performance liquid chromatography (UPLC)/MS analysis, and the differentially expressed metabolites were quantified using multiple reactions monitoring (MRM)/MS analysis in 44 patients and 42 HC. Pure standards were utilized to confirm the presence of the differentially expressed metabolites.

Results

Eighteen differentially expressed metabolites were identified in AOSD patents using LC/MS-based analysis, of which 13 metabolites were validated by MRM/MS analysis. Among them, serum levels of lysoPC(18:2), urocanic acid and indole were significantly lower, and L-phenylalanine levels were significantly higher in AOSD patients compared with HC. Moreover, serum levels of lysoPC(18:2), PhePhe, uridine, taurine, L-threonine, and (R)-3-Hydroxy-hexadecanoic acid were significantly correlated with disease activity scores (all p<0.05) in AOSD patients. A different clustering of metabolites was associated with a different disease outcome, with significantly lower levels of isovalerylsarcosine observed in patients with chronic articular pattern (median, 77.0AU/ml) compared with monocyclic (341.5AU/ml, p<0.01) or polycyclic systemic pattern (168.0AU/ml, p<0.05).

Conclusion

Thirteen differentially expressed metabolites identified and validated in AOSD patients were shown to be involved in five metabolic pathways. Significant associations of metabolic profiles with disease activity and outcome of AOSD suggest their involvement in AOSD pathogenesis.

NMR-based metabolomic approach to study urine samples of chronic inflammatory rheumatic disease patients

The nuclear magnetic resonance (NMR)-based metabolomic approach was used as analytical methodology to study the urine samples of chronic inflammatory rheumatic disease (CIRD) patients. The urine samples of CIRD patients were compared to the ones of both healthy subjects and patients with multiple sclerosis (MS), another immuno-mediated disease. Urine samples collected from 39 CIRD patients, 25 healthy subjects, and 26 MS patients were analyzed using ¹H NMR spectroscopy, and the NMR spectra were examined using partial least squares-discriminant analysis (PLS-DA). PLS-DA models were validated by a double cross-validation procedure and randomization tests. Clear discriminations between CIRD patients and healthy controls (average diagnostic accuracy 83.5 ± 1.9%) as well as between CIRD patients and MS patients (diagnostic accuracy 81.1 ± 1.9%) were obtained. Leucine, alanine, 3-hydroxyisobutyric acid, hippuric acid, citric acid, 3-hydroxyisovaleric acid, and creatinine contributed to the discrimination; all of them being in a lower concentration in CIRD patients as compared to controls or to MS patients. The application of NMR metabolomics to study these still poorly understood diseases can be useful to better clarify the pathologic mechanisms; moreover, as a holistic approach, it allowed the detection of, by means of anomalous metabolic traits, the presence of other pathologies or pharmaceutical treatments not directly connected to CIRDs, giving comprehensive information on the general health state of individuals. Graphical abstract NMR-based metabolomic approach as a tool to study urine samples in CIRD patients with respect to MS patients and healthy controls.

Technical Challenges in Mass Spectrometry-Based Metabolomics

Metabolomics is a strategy for analysis, and quantification of the complete collection of metabolites present in biological samples. Metabolomics is an emerging area of scientific research because there are many application areas including clinical, agricultural, and medical researches for the biomarker discovery and the metabolic system analysis by employing widely targeted analysis of a few hundred preselected metabolites from 10-100 biological samples. Further improvement in technologies of mass spectrometry in terms of experimental design for larger scale analysis, computational methods for tandem mass spectrometry-based elucidation of metabolites, and specific instrumentation for advanced bioanalysis will enable more comprehensive metabolome analysis for exploring the hidden secrets of metabolism.

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.

Metabolomic profiling predicts outcome of rituximab therapy in rheumatoid arthritis

OBJECTIVE

To determine whether characterisation of patients' metabolic profiles, utilising nuclear magnetic resonance (NMR) and mass spectrometry (MS), could predict response to rituximab therapy. 23 patients with active, seropositive rheumatoid arthritis (RA) on concomitant methotrexate were treated with rituximab. Patients were grouped into responders and non-responders according to the American College of Rheumatology improvement criteria, at a 20% level at 6 months. A Bruker Avance 700 MHz spectrometer and a Thermo Scientific Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometer were used to acquire (1)H-NMR and ultra high pressure liquid chromatography (UPLC)-MS/MS spectra, respectively, of serum samples before and after rituximab therapy. Data processing and statistical analysis were performed in MATLAB. 14 patients were characterised as responders, and 9 patients were considered non-responders. 7 polar metabolites (phenylalanine, 2-hydroxyvalerate, succinate, choline, glycine, acetoacetate and tyrosine) and 15 lipid species were different between responders and non-responders at baseline. Phosphatidylethanolamines, phosphatidyserines and phosphatidylglycerols were downregulated in responders. An opposite trend was observed in phosphatidylinositols. At 6 months, 5 polar metabolites (succinate, taurine, lactate, pyruvate and aspartate) and 37 lipids were different between groups. The relationship between serum metabolic profiles and clinical response to rituximab suggests that (1)H-NMR and UPLC-MS/MS may be promising tools for predicting response to rituximab.

Non-Esterified Fatty Acids Profiling in Rheumatoid Arthritis: Associations with Clinical Features and Th1 Response

Objectives

Since lipid compounds are known to modulate the function of CD4⁺ T-cells and macrophages, we hypothesize that altered levels of serum non-esterified fatty acids (NEFA) may underlie rheumatoid arthritis (RA) pathogenesis.

Methods

Serum levels of NEFA (palmitic, stearic, palmitoleic, oleic, linoleic, γ-linoleic, arachidonic –AA–, linolenic, eicosapentaenoic –EPA– and docosahexaenoic –DHA–) were quantified by LC-MS/MS after methyl-tert-butylether (MTBE)-extraction in 124 RA patients and 56 healthy controls (HC). CD4⁺ phenotype was studied by flow cytometry. TNFα, IL-8, VEGF, GM-CSF, IFNγ, IL-17, CCL2, CXCL10, leptin and resistin serum levels were quantified by immunoassays. The effect of FA on IFNγ production by PBMC was evaluated in vitro.

Results

Lower levels of palmitic (p<0.0001), palmitoleic (p = 0.002), oleic (p = 0.010), arachidonic (p = 0.027), EPA (p<0.0001) and DHA (p<0.0001) were found in RA patients, some NEFA being altered at onset. Cluster analysis identified a NEFA profile (hallmarked by increased stearic and decreased EPA and DHA) overrepresented in RA patients compared to HC (p = 0.002), being associated with clinical features (RF, shared epitope and erosions), increased IFNγ expression in CD4⁺ T-cells (p = 0.002) and a Th1-enriched serum milieu (IFNγ, CCL2 and CXCL10, all p<0.005). In vitro assays demonstrated that imbalanced FA could underlie IFNγ production by CD4⁺ T-cells. Finally, changes on NEFA levels were associated with clinical response upon TNFα-blockade.

Conclusion

An altered NEFA profile can be found in RA patients associated with clinical characteristics of aggressive disease and enhanced Th1 response. These results support the relevance of lipidomic studies in RA and provide a rationale for new therapeutic targets.