(1)H NMR-based metabolomic analysis for identifying serum biomarkers to evaluate methotrexate treatment in patients with early rheumatoid arthritis

Metabolomic Alterations in Methotrexate Treatment of Moderate-to-Severe Psoriasis

BACKGROUND Aberrant lipid metabolism alterations in skin tissue, blood, or urine have been implicated in psoriasis. Here, we examined lipid metabolites related to psoriasis and their association with the age of disease onset. MATERIAL AND METHODS Differences in lipid metabolites before and after methotrexate (MTX) treatment were evaluated. The discovery cohort and validation cohort consisted of 50 and 46 patients, respectively, with moderate-to-severe psoriasis. After MTX treatment, the patients were divided into response (Psoriasis Area and Severity Index [PASI] 75 and above) and non-response (PASI below 75) groups, blood was collected for serum metabolomics, and multivariate statistical analysis was performed. RESULTS We detected 1546 lipid metabolites. The proportion of the top 3 metabolites was as follows: triglycerides (TG, 34.8%), phospholipids (PE, 14.5%), phosphatidylcholine (PC, 12.4%); diglycerides (DG) (16: 1/18: 1), and DG (18: 1/18: 1) showed strong positive correlations with onset age. There were marked changes in TG (16: 0/18: 0/20: 0), TG (18: 0/18: 0/22: 0), TG (14: 0/18: 0/22: 0), TG (14: 0/20: 0/20: 0), lysophosphatidylcholine (LPC) (16: 0/0: 0), LPC (18: 0/0: 0), LPC (14: 0/0: 0), and LPC (18: 1/0: 0) levels before and after 12 weeks of MTX treatment. The glycerophospholipid metabolic pathway was implicated in psoriasis development. Of the 96 recruited patients, 35% were MTX responders and 65% non-responders. PE (34: 4) and PE (38: 1) levels were significantly different between the groups. Obvious differences in lipid metabolism were found between early-onset (<40 years) and late-onset (≥40 years) psoriasis. Significant changes in serum lipid profile before and after MTX treatment were observed. CONCLUSIONS The specific lipid level changes in responders may serve as an index for MTX treatment efficacy evaluation.

A serum metabolic biomarker panel for early rheumatoid arthritis

Objective

There is an urgent need for novel biomarkers to improve the early diagnosis of rheumatoid arthritis (ERA). Current serum biomarkers used in the management of ERA, including rheumatoid factor and anti-cyclic citrullinated peptide (ACPA), show limited specificity and sensitivity. Here, we used metabolomics to uncover new serum biomarkers of ERA.

Methods

We applied an untargeted metabolomics approach including gas chromatography time-of-flight mass spectrometry in serum samples from an ERA cohort (n=32) and healthy controls (n=19). Metabolite set enrichment analysis was performed to explore potentially important biological pathways. Partial least squares discriminant analysis and variable importance in projection analysis were performed to construct an ERA biomarker panel.

Results

Significant differences in the content of 11/81 serum metabolites were identified in patients with ERA. Receiver operating characteristic (ROC) analysis showed that a panel of only three metabolites (glyceric acid, lactic acid, and 3-hydroxisovaleric acid) could correctly classify 96.7% of patients with ERA, with an area under the ROC curve of 0.963 and with 94.4% specificity and 93.5% sensitivity, outperforming ACPA-based diagnosis by 2.9% and, thus, improving the preclinical detection of ERA. Aminoacyl-tRNA biosynthesis and serine, glycine, and phenylalanine metabolism were the most significant dysregulated pathways in patients with ERA.

Conclusion

A metabolomics serum-based biomarker panel composed of glyceric acid, lactic acid, and 3-hydroxisovaleric acid offers potential for the early clinical diagnosis of RA.

Plasma metabolomic profiling as a tool to identify predictive biomarkers of methotrexate efficacy in rheumatoid arthritis

OBJECTIVE

Methotrexate (MTX) remains the first-choice disease-modifying therapy in rheumatoid arthritis (RA). However, clinical response is variable, and no reliable predictive biomarkers of efficacy currently exist. In this study, plasma metabolomic profiling is evaluated as a tool to identify pretreatment biomarkers of MTX response in RA.

METHODS

Plasma collected from RA patients initiating MTX therapy (n = 20) were analyzed by metabolomic profiling totaling 648 identified metabolites. Pretreatment metabolomic profiles were compared based on clinical response after 16-weeks of MTX therapy. Clinical response to MTX was defined by a clinically meaningful reduction in disease activity score in 28 joints (DAS28-ESR) of greater than 1.2.

RESULTS

Pretreatment plasma levels of 19 metabolites were found to differ (p < 0.05) between RA patients based on response to MTX at 16-weeks. Spearman's correlation of pretreatment plasma metabolite levels with change in DAS28-ESR over the treatment period further supported three of the identified metabolites as associated with MTX response (p < 0.05). The identified metabolite levels were all found to be lower in RA patients responsive to MTX but were not found to be intercorrelated. Receiver operating characteristic analysis of each of the identified metabolites, alone or in combination, demonstrated an excellent discrimination between responders and non-responders based on pretreatment plasma levels of nornicotine (AUC = 0.84), N-methylisoleucine (AUC = 0.82), 2,3-dihydroxybutanoic acid (AUC = 0.82), and a combination biomarker panel score (AUC = 0.98).

CONCLUSION

Pretreatment plasma metabolomic profiling identified multiple metabolites associated with early response to MTX therapy in RA and represents a promising approach for the identification of clinical biomarkers of MTX response in RA.

Metabolomics in rheumatoid arthritis: Advances and review

Rheumatoid arthritis (RA) is an autoimmune disease accompanied by metabolic alterations. The metabolic profiles of patients with RA can be determined using targeted and non-targeted metabolomics technology. Metabolic changes in glucose, lipid, and amino acid levels are involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, the arachidonic acid metabolic pathway, and amino acid metabolism. These alterations in metabolic pathways and metabolites can fulfill bio-energetic requirements, promote cell proliferation, drive inflammatory mediator secretion, mediate leukocyte infiltration, induce joint destruction and muscle atrophy, and regulate cell proliferation, which may reflect the etiologies of RA. Differential metabolites can be used as biomarkers for the diagnosis, prognosis, and risk prediction, improving the specificity and accuracy of diagnostics and prognosis prediction. Additionally, metabolic changes associated with therapeutic responses can improve the understanding of drug mechanism. Metabolic homeostasis and regulation are new therapeutic strategies for RA. In this review, we provide a comprehensive overview of advances in metabolomics for RA.

A Key Metabolic Regulator of Bone and Cartilage Health

Taurine, a cysteine-derived zwitterionic sulfonic acid, is a common ingredient in energy drinks and is naturally found in fish and other seafood. In humans, taurine is produced mainly in the liver, and it can also be obtained from food. In target tissues, such as the retina, heart, and skeletal muscle, it functions as an essential antioxidant, osmolyte, and antiapoptotic agent. Taurine is also involved in energy metabolism and calcium homeostasis. Taurine plays a considerable role in bone growth and development, and high-profile reports have demonstrated the importance of its metabolism for bone health. However, these reports have not been collated for more than 10 years. Therefore, this review focuses on taurine-bone interactions and covers recently discovered aspects of taurine's effects on osteoblastogenesis, osteoclastogenesis, bone structure, and bone pathologies (e.g., osteoporosis and fracture healing), with due attention to the taurine-cartilage relationship.

Metabolomic Profiling Identifies Exogenous and Microbiota-Derived Metabolites as Markers of Methotrexate Efficacy in Juvenile Idiopathic Arthritis

Variability in methotrexate (MTX) efficacy represents a barrier to early and effective disease control in the treatment of juvenile idiopathic arthritis (JIA). This work seeks to understand the impact of MTX on the plasma metabolome and to identify metabolic biomarkers of MTX efficacy in a prospective cohort of children with JIA. Plasma samples from a cohort of children with JIA (n = 30) collected prior to the initiation of MTX and after 3 months of therapy were analyzed using a semi-targeted global metabolomic platform detecting 673 metabolites across a diversity of biochemical classes. Disease activity was measured using the 71-joint count juvenile arthritis disease activity score (JADAS-71) and clinical response to MTX was based on achievement of ACR Pedi 70 response. Metabolomic analysis identified 50 metabolites from diverse biochemical classes that were altered following the initiation of MTX (p < 0.05) with 15 metabolites reaching a false-discovery rate adjusted p-value (q-value) of less than 0.05. Enrichment analysis identified a class-wide reduction in unsaturated triglycerides following initiation of MTX (q = 0.0009). Twelve of the identified metabolites were significantly associated with disease activity by JADAS-71. Reductions in three metabolites were found to be associated with clinical response by ACR Pedi 70 response criteria and represented several microbiota and exogenously derived metabolites including: dehydrocholic acid, biotin, and 4-picoline. These findings support diverse metabolic changes following initiation of MTX in children with JIA and identify metabolites associated with microbial metabolism and exogenous sources associated with MTX efficacy.

Jintiange Capsule Alleviates Rheumatoid Arthritis and Reverses Changes of Serum Metabolic Profile in Collagen-Induced Arthritic Rats

Purpose

Jintiange capsule (JTG), an approved drug developed as a substitute for tiger bone (TB), has been clinically applied for osteoporosis therapy since 2003. The drug is composed of bionic TB powder, in which peptides and proteins are primarily enriched from other bone extracts. However, as a precious material of traditional Chinese medicine (TCM), TB has been mainly understood and used in TCM to relieve osteoporosis, rheumatoid arthritis and bone injury. Inspired by those, the purpose of this study was to investigate whether JTG also had an effect on relieving rheumatoid arthritis in collagen-induced arthritic (CIA) rats and explore potential mechanism from the perspective of serum metabolic profile changes.

Methods

JTG was analyzed using Nano LC-MS/MS and orally administered in CIA rats for 6 weeks. After administration, intervention effects of JTG on synovial inflammation, bone micro-architecture and bone metabolism were studied, and the impact of JTG on serum metabolic profiles in CIA rats was investigated by metabolomics.

Results

Nine bioactive peptides were identified in JTG. In animal treatments, JTG alleviated paw swelling (P < 0.01), arthritic severity (P < 0.01) and synovial tissue proliferation, as well as inflammatory cell infiltration of ankle joint, decreased bone loss, improved microstructure of bone in CIA rats by regulating bone absorption and formation, specifically increasing bone mineral density (BMD) (P < 0.05), bone volume fraction (BVF) (P < 0.05), trabecular number (Tb.N) (P < 0.05) and decreasing trabecular separation (Tb.Sp) (P < 0.05). Besides, serum IL-6 was down-regulated remarkably in CIA rats (P < 0.05). Furthermore, metabolomics analysis revealed that 32 metabolites were regulated significantly (P < 0.05) by comparison between CIA model and JTG in 360 mg/kg dose. The pathway analysis implied that JTG was involved in regulation of biosynthesis of phenylalanine.

Conclusion

JTG alleviates rheumatoid arthritis and reverses changes in serum metabolic profile in CIA rats.

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Plasma Metabolome Normalization in Rheumatoid Arthritis Following Initiation of Methotrexate and the Identification of Metabolic Biomarkers of Efficacy

Methotrexate (MTX) efficacy in the treatment of rheumatoid arthritis (RA) is variable and unpredictable, resulting in a need to identify biomarkers to guide drug therapy. This study evaluates changes in the plasma metabolome associated with response to MTX in RA with the goal of understanding the metabolic basis for MTX efficacy towards the identification of potential metabolic biomarkers of MTX response. Plasma samples were collected from healthy control subjects (n = 20), and RA patients initiating MTX therapy (n = 20, 15 mg/week) before and after 16 weeks of treatment. The samples were analyzed by a semi-targeted metabolomic analysis, and then analyzed by univariate and multivariate methods, as well as an enrichment analysis. An MTX response was defined as a clinically significant reduction in the disease activity score in 28 joints (DAS-28) of greater than 1.2; achievement of clinical remission, defined as a DAS-28 < 2.6, was also utilized as an additional measure of response. In this study, RA is associated with an altered plasma metabolome that is normalized following initiation of MTX therapy. Metabolite classes found to be altered in RA and corrected by MTX therapy were diverse and included triglycerides (p = 1.1 × 10⁻¹⁶), fatty acids (p = 8.0 × 10⁻¹²), and ceramides (p = 9.8 × 10⁻¹³). Stratification based on responses to MTX identified various metabolites differentially impacted in responders and non-responders including glucosylceramides (GlcCer), phosphatidylcholines (PC), sphingomyelins (SM), phosphatidylethanolamines (PE), choline, inosine, hypoxanthine, guanosine, nicotinamide, and itaconic acid (p < 0.05). In conclusion, RA is associated with significant alterations to the plasma metabolome displaying at least partial normalization following 16 weeks of MTX therapy. Changes in multiple metabolites were found to be associated with MTX efficacy, including metabolites involved in fatty acid/lipid, nucleotide, and energy metabolism.

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.

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).

Pharmacometabolomics by NMR in Oncology: A Systematic Review

Pharmacometabolomics (PMx) studies aim to predict individual differences in treatment response and in the development of adverse effects associated with specific drug treatments. Overall, these studies inform us about how individuals will respond to a drug treatment based on their metabolic profiles obtained before, during, or after the therapeutic intervention. In the era of precision medicine, metabolic profiles hold great potential to guide patient selection and stratification in clinical trials, with a focus on improving drug efficacy and safety. Metabolomics is closely related to the phenotype as alterations in metabolism reflect changes in the preceding cascade of genomics, transcriptomics, and proteomics changes, thus providing a significant advance over other omics approaches. Nuclear Magnetic Resonance (NMR) is one of the most widely used analytical platforms in metabolomics studies. In fact, since the introduction of PMx studies in 2006, the number of NMR-based PMx studies has been continuously growing and has provided novel insights into the specific metabolic changes associated with different mechanisms of action and/or toxic effects. This review presents an up-to-date summary of NMR-based PMx studies performed over the last 10 years. Our main objective is to discuss the experimental approaches used for the characterization of the metabolic changes associated with specific therapeutic interventions, the most relevant results obtained so far, and some of the remaining challenges in this area.

Prediction of response of methotrexate in patients with rheumatoid arthritis using serum lipidomics

Methotrexate (MTX) is a common first-line treatment for new-onset rheumatoid arthritis (RA). However, MTX is ineffective for 30–40% of patients and there is no way to know which patients might benefit. Here, we built statistical models based on serum lipid levels measured at two time-points (pre-treatment and following 4 weeks on-drug) to investigate if MTX response (by 6 months) could be predicted. Patients about to commence MTX treatment for the first time were selected from the Rheumatoid Arthritis Medication Study (RAMS). Patients were categorised as good or non-responders following 6 months on-drug using EULAR response criteria. Serum lipids were measured using ultra‐performance liquid chromatography–mass spectrometry and supervised machine learning methods (including regularized regression, support vector machine and random forest) were used to predict EULAR response. Models including lipid levels were compared to models including clinical covariates alone. The best performing classifier including lipid levels (assessed at 4 weeks) was constructed using regularized regression (ROC AUC 0.61 ± 0.02). However, the clinical covariate based model outperformed the classifier including lipid levels when either pre- or on-treatment time-points were investigated (ROC AUC 0.68 ± 0.02). Pre- or early-treatment serum lipid profiles are unlikely to inform classification of MTX response by 6 months with performance adequate for use in RA clinical management.

Identification of Metabolic Biomarkers in Relation to Methotrexate Response in Early Rheumatoid Arthritis

This study aimed to identify baseline metabolic biomarkers for response to methotrexate (MTX) therapy in rheumatoid arthritis (RA) using an untargeted method. In total, 82 baseline plasma samples (41 insufficient responders and 41 sufficient responders to MTX) were selected from the Treatment in the Rotterdam Early Arthritis Cohort (tREACH, trial number: ISRCTN26791028) based on patients' EULAR response at 3 months. Metabolites were assessed using high-performance liquid chromatography-quadrupole time of flight mass spectrometry. Differences in metabolite concentrations between insufficient and sufficient responders were assessed using partial least square regression discriminant analysis (PLS-DA) and Welch's t-test. The predictive performance of the most significant findings was assessed in a receiver operating characteristic plot with area under the curve (AUC), sensitivity and specificity. Finally, overrepresentation analysis was performed to assess if the best discriminating metabolites were enriched in specific metabolic events. Baseline concentrations of homocystine, taurine, adenosine triphosphate, guanosine diphosphate and uric acid were significantly lower in plasma of insufficient responders versus sufficient responders, while glycolytic intermediates 1,3-/2,3-diphosphoglyceric acid, glycerol-3-phosphate and phosphoenolpyruvate were significantly higher in insufficient responders. Homocystine, glycerol-3-phosphate and 1,3-/2,3-diphosphoglyceric acid were independent predictors and together showed a high AUC of 0.81 (95% CI: 0.72-0.91) for the prediction of insufficient response, with corresponding sensitivity of 0.78 and specificity of 0.76. The Warburg effect, glycolysis and amino acid metabolism were identified as underlying metabolic events playing a role in clinical response to MTX in early RA. New metabolites and potential underlying metabolic events correlating with MTX response in early RA were identified, which warrant validation in external cohorts.

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.

In search of pathobiological endotypes: a systems approach to early rheumatoid arthritis

INTRODUCTION

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease. Early referral and treatment are key to the effective management of the disease. This makes imperative the identification of biomarkers and of pathobiological endotypes.

AREAS COVERED

This review describes recent efforts to integrate large-scale datasets for the identification of disease endotypes for precision medicine in early, seropositive RA. We conducted a search for systems and multi-omics papers in early RA patients through to 1 January 2020. We reviewed investigations of multiple technologies such as transcriptomic, proteomic and metabolomic platforms as well as extensive clinical datasets. We outline progress made and describe some of the advantages and limitations of current computational and statistical methods.

EXPERT OPINION

The search for pathobiological endotypes in early RA is rapidly developing. While currently, studies tend to be small, reliant upon new technologies and unproven analytical tools, as the technology becomes cheaper and more reliable, and the properties of analytical tools for the integration of cross-platform biology become better understood, it seems likely that better biomarkers of disease, remission and response to individual therapies will emerge.

Can joint fluid metabolic profiling (or "metabonomics") reveal biomarkers for osteoarthritis and inflammatory joint disease?: A systematic review

Aims

Metabolic profiling is a top-down method of analysis looking at metabolites, which are the intermediate or end products of various cellular pathways. Our primary objective was to perform a systematic review of the published literature to identify metabolites in human synovial fluid (HSF), which have been categorized by metabolic profiling techniques. A secondary objective was to identify any metabolites that may represent potential biomarkers of orthopaedic disease processes.

Methods

A systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines using the MEDLINE, Embase, PubMed, and Cochrane databases. Studies included were case series, case control series, and cohort studies looking specifically at HSF.

Results

The primary analysis, which pooled the results from 17 published studies and four meeting abstracts, identified over 200 metabolites. Seven of these studies (six published studies, one meeting abstract) had asymptomatic control groups and collectively suggested 26 putative biomarkers in osteoarthritis, inflammatory arthropathies, and trauma. These can broadly be categorized into amino acids plus related metabolites, fatty acids, ketones, and sugars.

Conclusion

The role of metabolic profiling in orthopaedics is fast evolving with many metabolites already identified in a variety of pathologies. However, these results need to be interpreted with caution due to the presence of multiple confounding factors in many of the studies. Future research should include largescale epidemiological metabolic profiling studies incorporating various confounding factors with appropriate statistical analysis to account for multiple testing of the data.

Cite this article:Bone Joint Res. 2020;9(3):108–119.

Medium and long-term effects of low doses of Chlorpyrifos during the postnatal, preweaning developmental stage on sociability, dominance, gut microbiota and plasma metabolites

Autism spectrum disorder (ASD) is a complex neurodevelopmental pathology characterized by altered verbalizations, reduced social interaction behavior, and stereotypies. Environmental factors have been associated with its development. Some researchers have focused on pesticide exposure. Chlorpyrifos (CPF) is the most used Organophosphate. Previous developmental studies with CPF showed decreased, enhanced or no effect on social outcomes eminently in mice. The study of CPF exposure during preweaning stages on social behavior is sparse in mice and non-existent in rats. d stressors could be at the basis of ASD development, and around postnatal day 10 in the rat is equivalent to the human birthday in neurodevelopmental terms. We explored the effects of exposure to low doses (1mg/kg/mL/day) of CPF during this stage regarding: sociability, dominance gut microbiome and plasma metabolomic profile, since alterations in these systems have also been linked to ASD. There was a modest influence of CPF on social behavior in adulthood, with null effects during adolescence. Dominance and hierarchical status were not affected by exposure. Dominance status explained the significant reduction in reaction to social novelty observed on the sociability test. CPF induced a significant gut microbiome dysbiosis and triggered a hyperlipidemic, hypoglycemic/hypogluconeogenesis and a general altered cell energy production in females. These behavioral results in rats extend and complement previous studies with mice and show novel influences on gut metagenomics and plasma lipid profile and metabolomics, but do not stablish a relation between the exposure to CPF and the ASD phenotype. The effects of dominance status on reaction to social novelty have an important methodological meaning for future research on sociability.

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.

Differences in the serum metabolome and lipidome identify potential biomarkers for seronegative rheumatoid arthritis versus psoriatic arthritis

Objectives

The differential diagnosis of seronegative rheumatoid arthritis (negRA) and psoriasis arthritis (PsA) is often difficult due to the similarity of symptoms and the unavailability of reliable clinical markers. Since chronic inflammation induces major changes in the serum metabolome and lipidome, we tested whether differences in serum metabolites and lipids could aid in improving the differential diagnosis of these diseases.

Methods

Sera from negRA and PsA patients with established diagnosis were collected to build a biomarker-discovery cohort and a blinded validation cohort. Samples were analysed by proton nuclear magnetic resonance. Metabolite concentrations were calculated from the spectra and used to select the variables to build a multivariate diagnostic model.

Results

Univariate analysis demonstrated differences in serological concentrations of amino acids: alanine, threonine, leucine, phenylalanine and valine; organic compounds: acetate, creatine, lactate and choline; and lipid ratios L3/L1, L5/L1 and L6/L1, but yielded area under the curve (AUC) values lower than 70%, indicating poor specificity and sensitivity. A multivariate diagnostic model that included age, gender, the concentrations of alanine, succinate and creatine phosphate and the lipid ratios L2/L1, L5/L1 and L6/L1 improved the sensitivity and specificity of the diagnosis with an AUC of 84.5%. Using this biomarker model, 71% of patients from a blinded validation cohort were correctly classified.

Conclusions

PsA and negRA have distinct serum metabolomic and lipidomic signatures that can be used as biomarkers to discriminate between them. After validation in larger multiethnic cohorts this diagnostic model may become a valuable tool for a definite diagnosis of negRA or PsA patients.

Metabolomic Profiling to Identify Molecular Biomarkers of Cellular Response to Methotrexate In Vitro

Variation in methotrexate (MTX) efficacy represents a significant barrier to early and effective disease control in the treatment of autoimmune arthritis. We hypothesize that the utilization of metabolomic techniques will allow for an improved understanding of the biochemical basis for the pharmacological activity of MTX, and can promote the identification and evaluation of novel molecular biomarkers of MTX response. In this work, erythroblastoid cells were exposed to MTX at the physiologic concentration of 1,000 nM and analyzed using three metabolomic platforms to give a broad spectrum of cellular metabolites. MTX pharmacological activity, defined as cellular growth inhibition, was associated with an altered cellular metabolomic profile based on the analysis of 724 identified metabolites. By discriminant analysis, MTX treatment was associated with increases in ketoisovaleric acid, fructose, galactose, and 2-deoxycytidine, and corresponding reductions in 2-deoxyuridine, phosphatidylinositol 32:0, orotic acid, and inosine monophosphate. Inclusion of data from analysis of folate metabolism in combination with chemometric and metabolic network analysis demonstrated that MTX treatment is associated with dysregulated folate metabolism and nucleotide biosynthesis, which is in line with its known mechanism of action. However, MTX treatment was also associated with alterations in a diversity of metabolites, including intermediates of amino acid, carbohydrate, and lipid metabolism. Collectively, these findings support a robust metabolic response following exposure to physiologic concentrations of MTX. They also identify various metabolic intermediates that are associated with the pharmacological activity of MTX, and are, therefore, potential molecular biomarker candidates in future preclinical and clinical studies of MTX efficacy in autoimmune arthritis.

LC-MS metabolomics comparisons of cancer cell and macrophage responses to methotrexate and polymer-encapsulated methotrexate

Methotrexate (MTX) is a folate analogue antimetabolite widely used for the treatment of rheumatoid arthritis and cancer. A number of studies have shown that MTX delivered via nanoparticle carriers is more potent against cancer cells than free MTX, a phenomenon attributed to higher cellular uptake of the particles compared to the saturable folate receptor pathway. In this study, a cell-based global metabolic profiling approach was applied to study the effects of MTX in both free drug form and when encapsulated in -poly(lactide-co-glycolide) (PLGA) nanoparticles on a cancer cell line, A549, and also on human-like THP-1 macrophages. The results showed that MTX loaded nanoparticles had less impact on the macrophages than free MTX, and the effects on macrophages were limited to changes in nucleotide metabolism and suppression of the tricarboxylic acid cycle, whereas free MTX also led to a drop in glycolytic activity and impairment in redox homeostasis. In contrast, MTX loaded nanoparticles showed a greater impact on A549 cells than the free drug, which was in accord with studies in other cell lines in prior literature with MTX-carrier nanoparticles.

Is prediction of clinical response to methotrexate in individual rheumatoid arthritis patients possible? A systematic literature review

OBJECTIVES

To identify, by a systematic literature review, predictors of clinical response to methotrexate treatment in rheumatoid arthritis patients, which would facilitate personalised treatment.

METHODS

PubMed and Embase databases were searched for original articles. Additionally, congress abstracts of European League Against Rheumatism and American College of Rheumatology annual meetings of the past 2 years were screened. Articles describing predictors of clinical response to methotrexate after 3 to 6 months were included, since this reflects the time span used to determine treatment effectiveness and decide on treatment changes in treat-to-target recommendations.

RESULTS

Thirty articles were included, containing 100 different predictors and 11 predictive models. Nineteen predictors and 2 predictive models were studied in multiple cohorts. Female gender was found to be a predictor of non-response in two studies (odds ratios 0.55 and 0.54), but these findings could not be replicated in two other studies. In two studies, smoking predicted non-response (adjusted odds ratios 0.35 and 0.60), although this was inconsistent over all response criteria assessed. Rheumatoid factor positivity predicted non-response in two studies (adjusted hazard ratio 0.61, adjusted odds ratio 0.4), but this was not found in three other studies. Heterogeneity in studies prohibited further comparison of predictive values between studies. Additionally, a validated epigenetic model was found (area under the curve 0.90 and 0.91).

CONCLUSIONS

No predictors were identified reliably predicting clinical response to methotrexate after 3 to 6 months in the individual patient: clinical predictors were weak. However, a promising epigenetic model was found that needs further validation.

Integrative metabolomics reveals unique metabolic traits in Guillain-Barré Syndrome and its variants

Guillain-Barré syndrome (GBS) is an acute fatal progressive disease caused by autoimmune mechanism mainly affecting peripheral nervous system. Although the syndrome is clinically sub-classified into several variants, specific biomarker and exact pathomechanism of each subtypes are not well elucidated yet. In current study, integrative metabolomic and lipidomic profiles were acquisitioned from cerebrospinal fluid samples of 86 GBS from three variants and 20 disease controls. And the data were systematically compared to our previous result on inflammatory demyelination disorders of central nervous system (IDDs) and healthy controls. Primary metabolite profiles revealed unique metabolic traits in which 9 and 7 compounds were specifically changed in GBS and IDD, respectively. Next, the biomarker panel with 10 primary metabolites showed a fairly good discrimination power among 3 GBS subtypes, healthy controls, and disease controls (AUCs ranged 0.849-0.999). The robustness of the biomarker panel was vigorously validated by multi-step statistical evaluation. Subsequent lipidomics revealed GBS variant-specific alteration where the significant elevations of lyso-phosphatidylcholines and sphingomyelins were unique to AIDP (acute inflammatory demyelinating polyneuropathy) and AMAN (acute motor axonal neuropathy), respectively. And metabolome-wide multivariate correlation analysis identified potential clinical association between GBS disability scale (Hughes score) and CSF lipids (monoacylglycerols, and sphingomyelins). Finally, Bayesian network analysis of covarianced structures of primary metabolites and lipids proposed metabolic hub and potential biochemical linkage associated with the pathology.

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.

Cardiovascular effects of methotrexate in immune-mediated inflammatory diseases

The cardiovascular effects of disease-modifying antirheumatic drugs and particularly of methotrexate (MTX) are complex and frequently incorrectly understood, which might lead to the unjustified discontinuation of this treatment. MTX, 'the gold standard' and first line treatment in rheumatoid arthritis, psoriatic arthritis, and other immune-mediated inflammatory diseases, has been proven to decrease inflammation, improve cardiovascular risk factors, and reduce mortality. This is supported by both the mechanism of action, as well as a body of clinical data evidence. MTX's cardiovascular effects, although incompletely understood, are explained by its antiproliferative, immunosuppressive, anti-inflammatory, and antiatherogenic effects. Several clinical trials have shown that MTX is associated with improved endothelial function, slower atherosclerosis progression, decreased risk of major cardiovascular adverse events, and benefits on survival. Given its systemic cardiovascular effects, MTX could be regarded as an important therapeutic agent not only to control disease activity in rheumatic diseases, but also to reduce cardiovascular risk and mortality.

Treatment of Rheumatoid Arthritis Using Combination of Methotrexate and Tripterygium Glycosides Tablets-A Quantitative Plasma Pharmacochemical and Pseudotargeted Metabolomic Approach

Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by chronic destructive synovitis and is associated with progressive disability, systemic difficulties, premature death, and socioeconomic costs. Early intervention with disease-modifying antirheumatic drugs (DMARDs) like methotrexate (MTX) and its combination regimen would provide obvious benefits to patients, healthcare systems and society. MTX and tripterygium glycosides tablets (TGT_S) are most frequently prescribed medicines for RA, and the combination of them occurs frequently in anti-RA prescriptions. While the underlying combination mechanisms and the affected variation of drug blood level remain unclear. According to the American College of Rheumatology criteria for improvement, clinical evaluation following three treatment groups (i.e., MTX and TGT_S mono- and combined groups) were carried out at baseline and at the end of 12 weeks in a randomized controlled clinical trial. To monitor the affected variation of drug blood level and perturbation of metabolites caused by MTX plus TGT_S combined to treat active RA, the collected plasma samples were analyzed using RRLC-QqQ-MS and UHPLC-QE Orbitrap HRMS instruments. As a result, 39 metabolites including 7 MTX-related metabolites, 13 TGT_S-related migratory ingredients and 19 characteristic endogenous metabolites, were quantitatively determined in plasma samples of RA patients after oral administration. The potential mechanism of MTX and TGT_S combination were preliminarily elucidated on the aspect of clinical biochemical test indicators integrated with quantitative plasma pharmacochemistry and the pseudotargeted metabolomics.

1H NMR Metabolomics Identifies Underlying Inflammatory Pathology in Osteoarthritis and Rheumatoid Arthritis Synovial Joints

Despite osteoarthritis (OA) and rheumatoid arthritis (RA) being typically age-related, their underlying etiologies are markedly different. We used ¹H nuclear magnetic resonance (NMR) spectroscopy to identify differences in metabolite profiles in low volumes of OA and RA synovial fluid (SF). SF was aspirated from knee joints of 10 OA and 14 RA patients. 100 μL SF was analyzed using a 700 MHz Avance IIIHD Bruker NMR spectrometer with a TCI cryoprobe. Spectra were analyzed by Chenomx, Bruker TopSpin and AMIX software. Statistical analysis was undertaken using Metaboanalyst. 50 metabolites were annotated, including amino acids, saccharides, nucleotides and soluble lipids. Discriminant analysis identified group separation between OA and RA cohorts, with 32 metabolites significantly different between OA and RA SF (false discovery rate (FDR) < 0.05). Metabolites of glycolysis and the tricarboxylic acid cycle were lower in RA compared to OA; these results concur with higher levels of inflammation, synovial proliferation and hypoxia found in RA compared to OA. Elevated taurine in OA may indicate increased subchondral bone sclerosis. We demonstrate that quantifiable differences in metabolite abundance can be measured in low volumes of SF by ¹H NMR spectroscopy, which may be clinically useful to aid diagnosis and improve understanding of disease pathogenesis.

1H NMR-based metabolomic fingerprinting to determine metabolite levels in serrano peppers (Capsicum annum L.) grown in two different regions

Chili pepper (Capsicum annuum) is the most important and emblematic condiment in Mexican food. Serrano pepper is a variety of C. annuum that is traditionally cultivated in Mexico and commercialized in local markets. The aim of this study was to describe the ¹H NMR metabolomic profiling of the aqueous phase of serrano peppers harvested from two distinct regions, in the states of Veracruz and Oaxaca, Mexico. According to the current results, aspartate citrate, lactate, leucine and sucrose were found at higher amount in the serrano peppers from Veracruz. On the other hand, acetate, formate, fumarate, malonate, phosphocholine, pyruvate and succinate showed the highest abundance in this product from Oaxaca. These are the main metabolites that distinguish one group from the other. The spectrometric method reported presently is characterized by great simplicity, robustness and reproducibility. Thus, this technique can be used for establishing reliable metabolomic fingerprints of serrano peppers grown under different environmental conditions.

Application of omics in predicting anti-TNF efficacy in rheumatoid arthritis

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by progressive joint erosion. Tumor necrosis factor (TNF) antagonists are the most widely used biological disease-modifying anti-rheumatic drug in RA. However, there continue to be one third of RA patients who have poor or no response to TNF antagonists. Following consideration of the uncertainty of therapeutic effects and the high price of TNF antagonists, it is worthy to predict the treatment responses before anti-TNF therapy. According to the comparisons between the responders and non-responders to TNF antagonists by omic technologies, such as genomics, transcriptomics, proteomics, and metabolomics, rheumatologists are eager to find significant biomarkers to predict the effect of TNF antagonists in order to optimize the personalized treatment in RA.

Culturomics: A New Kid on the Block of OMICS to Enable Personalized Medicine

This innovation analysis highlights the underestimated and versatile potential of the new field of culturomics and examines its relation to other OMICS system sciences such as infectiomics, metabolomics, phenomics, and pharmacomicrobiomics. The advent of molecular biology, followed by the emergence of various disciplines of the genomics, and most importantly metagenomics, brought about the sharp decline of conventional microbiology methods. Emergence of culturomics has a natural synergy with therapeutic and clinical genomic approaches so as to realize personalized medicine. Notably, the concept of culturomics expands on that of phenomics and allows a reintroduction of the culture-based phenotypic characterization into the 21st century research repertoire, bolstered by robust technology for automated and massive execution, but its potential is largely unappreciated at present; the few available references show unenthusiastic pursuit and in narrow applications. This has not to be so: depending on the specific brand of culturomics, the scope of applications may extend to medicine, agriculture, environmental sciences, pharmacomicrobiomics, and biotechnology innovation. Moreover, culturomics may produce Big Data. This calls for a new generation of data scientists and innovative ways of harnessing and valorizing Big Data beyond classical genomics. Much more detailed and objective classification and identification of microbiota may soon be at hand through culturomics, thus enabling precision diagnosis toward truly personalized medicine. Culturomics may both widen the scope of microbiology and improve its contributions to diagnostics and personalized medicine, characterizing microbes and determining their associations with health and disease dynamics.

The Personalization of Clopidogrel Antiplatelet Therapy: The Role of Integrative Pharmacogenetics and Pharmacometabolomics

Dual antiplatelet therapy of aspirin and clopidogrel is pivotal for patients undergoing percutaneous coronary intervention. However, the variable platelets reactivity response to clopidogrel may lead to outcome failure and recurrence of cardiovascular events. Although many genetic and nongenetic factors are known, great portion of clopidogrel variable platelets reactivity remain unexplained which challenges the personalization of clopidogrel therapy. Current methods for clopidogrel personalization include CYP2C19 genotyping, pharmacokinetics, and platelets function testing. However, these methods lack precise prediction of clopidogrel outcome, often leading to insufficient prediction. Pharmacometabolomics which is an approach to identify novel biomarkers of drug response or toxicity in biofluids has been investigated to predict drug response. The advantage of pharmacometabolomics is that it does not only predict the response but also provide extensive information on the metabolic pathways implicated with the response. Integrating pharmacogenetics with pharmacometabolomics can give insight on unknown genetic and nongenetic factors associated with the response. This review aimed to review the literature on factors associated with the variable platelets reactivity response to clopidogrel, as well as appraising current methods for the personalization of clopidogrel therapy. We also aimed to review the literature on using pharmacometabolomics approach to predict drug response, as well as discussing the plausibility of using it to predict clopidogrel outcome.

Targeted Metabolic Profiling of the Tg197 Mouse Model Reveals Itaconic Acid as a Marker of Rheumatoid Arthritis

Rheumatoid arthritis is a progressive, highly debilitating disease where early diagnosis, enabling rapid clinical intervention, would provide obvious benefits to patients, healthcare systems, and society. Novel biomarkers that enable noninvasive early diagnosis of the onset and progression of the disease provide one route to achieving this goal. Here a metabolic profiling method has been applied to investigate disease development in the Tg197 arthritis mouse model. Hind limb extract profiling demonstrated clear differences in metabolic phenotypes between control (wild type) and Tg197 transgenic mice and highlighted raised concentrations of itaconic acid as a potential marker of the disease. These changes in itaconic acid concentrations were moderated or indeed reversed when the Tg197 mice were treated with the anti-hTNF biologic infliximab (10 mg/kg twice weekly for 6 weeks). Further in vitro studies on synovial fibroblasts obtained from healthy wild-type, arthritic Tg197, and infliximab-treated Tg197 transgenic mice confirmed the association of itaconic acid with rheumatoid arthritis and disease-moderating drug effects. Preliminary indications of the potential value of itaconic acid as a translational biomarker were obtained when studies on K4IM human fibroblasts treated with hTNF showed an increase in the concentrations of this metabolite.

Protective effect of higenamine ameliorates collagen-induced arthritis through heme oxygenase-1 and PI3K/Akt/Nrf-2 signaling pathways

Existing in Ranunculaceae Aconitum and tomato, with the chemical name 1-phydroxybenzyl-1,2,3,4-tetrahy-droisoquinoline, higenamine is widely distributed in China. Higenamine's anti-inflammatory, antioxidant and anti-apoptotic effects have been identified in previous studies. The present study attempted to determine the protective effect of higenamine against collagen-induced arthritis through heme oxygenase-1 (HO-1) and PI3K/Akt/Nrf-2 signaling pathways. A type II collagen (CII)-induced arthritis (CIA) model was established and clinical arthritis scores were used to appraise the curative effect of higenamine. Inflammatory reactions, oxidative damage and caspase-3/9 activation were detected using specific ELISA kits. In addition, western blotting was used to evaluate the expression of HO-1, Akt and Nrf-2 protein in CII-induced CIA mice. In CII-induced CIA mice, the clinical arthritis scores, inflammatory reactions, oxidation damage and caspase-3/9 activation were increased and activated. The results demonstrated that treatment with higenamine significantly reduced the elevation of clinical arthritis scores (P<0.01), and suppressed the promotion of inflammatory reactions, oxidation damage and caspase-3/9 activation. Furthermore, higenamine significantly increased HO-1 protein expression (P<0.01) and upregulated the PI3K/Akt/Nrf-2 signal pathway in CII-induced CIA mice. Collectively, it is concluded that higenamine protects against CII-induced CIA through the induction of HO-1 and the upregulation of the PI3K/Akt/Nrf-2 signaling pathway. In conclusion, higenamine may be a beneficial drug for protecting against CIA.

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.

Lipid and Metabolic Changes in Rheumatoid Arthritis

While the most obvious manifestations of rheumatoid arthritis (RA) involve inflammation and damage in the synovial joints, the systemic effects of the condition are widespread and life-threatening. Of particular interest is the 'lipid paradox' of RA, where patients with a numerically equivocal starting lipid profile have a significantly raised risk of cardiovascular (CV) events and response to therapy results in a 'normalization' of lipid levels and reduction in events. Changes in lipids can be seen before overt disease manifestations which suggest that they are closely linked to the more widespread inflammation-driven metabolic changes associated with tumour necrosis factor (TNF). Cachexia involves a shift in body mass from muscle to fat, which may or may not directly increase the cardiovascular risk. However, since TNF inhibition is associated with reduction in cardiovascular events, it does suggest that these widespread metabolic changes involving lipids are of importance. Analysis of single lipids or metabolites have been used to identify some of the key changes, but more recently, metabolomic and lipidomic approaches have been applied to identify a broad spectrum of small molecule changes and identify potentially altered metabolic pathways. Further work is needed to understand fully the metabolic changes in lipid profiles and identify novel ways of targeting desired profile changes, but work so far does suggest that a better understanding may allow management of patients to downregulate the systemic effects of their disease that puts them at risk of cardiovascular complications.

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.

Pharmacometabolomics-aided Pharmacogenomics in Autoimmune Disease

Inter-individual variability has been a major hurdle to optimize disease management. Precision medicine holds promise for improving health and healthcare via tailor-made therapeutic strategies. Herein, we outline the paradigm of "pharmacometabolomics-aided pharmacogenomics" in autoimmune diseases. We envisage merging pharmacometabolomic and pharmacogenomic data (to address the interplay of genomic and environmental influences) with information technologies to facilitate data analysis as well as sense- and decision-making on the basis of synergy between artificial and human intelligence. Humans can detect patterns, which computer algorithms may fail to do so, whereas data-intensive and cognitively complex settings and processes limit human ability. We propose that better-informed, rapid and cost-effective omics studies need the implementation of holistic and multidisciplinary approaches.

Management of rheumatoid arthritis: Impact and risks of various therapeutic approaches

Rheumatic diseases are highly prevalent chronic disorders and the leading cause of physical disability worldwide, with a marked socio-economic impact. Rheumatoid arthritis (RA) is a chronic systemic inflammatory disease of unknown etiology with an autoimmune pathogenesis, characterised by arthropathy with chronic, deforming, destructive evolution and multiple systemic manifestations. The management of RA has undergone significant changes as far as objectives and approaches are concerned, ending in the current strategy known as ‘treat to target’. The therapeutic array of RA includes several categories of medicinal products, of varying potential. There are several criteria for the classification of medicinal products used against this disease, one of the most important and modern of which divides such substances according to their effects on the progress of the disease: symptom-modifying antirheumatic drugs (including non-steroidal anti-inflammatory drugs and corticoids), disease-modifying antirheumatic drugs (including various substances, such as gold salts, antimalarials, sulfasalazine, D-penicillamine; non-specific immunosuppressive medication, such as methotrexate, cyclophosphamide, azathioprine and leflunomide) and biological therapy is a recent addition, providing new insight into the treatment of this disease. The selection of the optimal therapy for RA should be based on guidelines and recommendations, but also on clinical particular aspects and patient preferences.

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.

Metabolite Profiling of Diverse Rice Germplasm and Identification of Conserved Metabolic Markers of Rice Roots in Response to Long-Term Mild Salinity Stress

The sensitivity of rice to salt stress greatly depends on growth stages, organ types and cultivars. Especially, the roots of young rice seedlings are highly salt-sensitive organs that limit plant growth, even under mild soil salinity conditions. In an attempt to identify metabolic markers of rice roots responding to salt stress, metabolite profiling was performed by ¹H-NMR spectroscopy in 38 rice genotypes that varied in biomass accumulation under long-term mild salinity condition. Multivariate statistical analysis showed separation of the control and salt-treated rice roots and rice genotypes with differential growth potential. By quantitative analyses of ¹H-NMR data, five conserved salt-responsive metabolic markers of rice roots were identified. Sucrose, allantoin and glutamate accumulated by salt stress, whereas the levels of glutamine and alanine decreased. A positive correlation of metabolite changes with growth potential and salt tolerance of rice genotypes was observed for allantoin and glutamine. Adjustment of nitrogen metabolism in rice roots is likely to be closely related to maintain the growth potential and increase the stress tolerance of rice.

Metabolomics and its potential in diagnosis, prognosis and treatment of rheumatic diseases

The main aim of metabolomics is to make a comprehensive study of metabolites, the intermediates of biochemical processes in living organisms. Any pathophysiological mechanism caused by disease will inevitably lead to related changes in the concentrations of specific metabolites. In line with this, metabolomics offers a promising laboratory tool for the analysis of potential diagnostic biomarkers that may be used to assess susceptibility to a disease and to evaluate the prognosis and therapeutic response to treatment. Recent data have shown that metabolomics analysis in rheumatoid arthritis has made possible more efficient diagnosis, discrimination between patients with regard to disease activity, prediction of the response to a particular treatment approach, differentiation between rheumatic disease subtypes and greater understanding of the pathophysiology of this disease. Here we characterize metabolomics as a comprehensive laboratory tool and review its potential in the diagnosis, prognosis and treatment of rheumatic diseases such as rheumatoid arthritis.

Energy Metabolism Disorder as a Contributing Factor of Rheumatoid Arthritis: A Comparative Proteomic and Metabolomic Study

Objectives

To explore the pathogenesis of rheumatoid arthritis (RA), the different metabolites were screened in synovial fluid by metabolomics.

Methods

Synovial fluid from 25 RA patients and 10 normal subjects were analyzed by GC/TOF MS analysis so as to give a broad overview of synovial fluid metabolites. The metabolic profiles of RA patients and normal subjects were compared using multivariate statistical analysis. Different proteins were verified by qPCR and western blot. Different metabolites were verified by colorimetric assay kit in 25 inactive RA patients, 25 active RA patients and 20 normal subjects. The influence of hypoxia-inducible factor (HIF)-1α pathway on catabolism was detected by HIF-1α knockdown.

Results

A subset of 58 metabolites was identified, in which the concentrations of 7 metabolites related to energy metabolism were significantly different as shown by importance in the projection (VIP) (VIP≥1) and Student’s t-test (p<0.05). In the 7 metabolites, the concentration of glucose was decreased, and the concentration of lactic acid was increased in the synovial fluid of RA patients than normal subjects verified by colorimetric assay Kit. Receiver operator characteristic (ROC) analysis shows that the concentration of glucose and lactic acid in synovial fluid could be used as dependable biomarkers for the diagnosis of active RA, provided an AUC of 0.906 and 0.922. Sensitivity and specificity, which were determined by cut-off points, reached 84% and 96% in sensitivity and 95% and 85% in specificity, respectively. The verification of different proteins identified in our previous proteomic study shows that the enzymes of anaerobic catabolism were up-regulated (PFKP and LDHA), and the enzymes of aerobic oxidation and fatty acid oxidation were down-regulated (CS, DLST, PGD, ACSL4, ACADVL and HADHA) in RA patients. The expression of HIF-1α and the enzymes of aerobic oxidation and fatty acid oxidation were decreased and the enzymes of anaerobic catabolism were increased in FLS cells after HIF-1α knockdown.

Conclusion

It was found that enhanced anaerobic catabolism and reduced aerobic oxidation regulated by HIF pathway are newly recognized factors contributing to the progression of RA, and low glucose and high lactic acid concentration in synovial fluid may be the potential biomarker of RA.

The gut microbiome in autoimmunity: Sex matters

Autoimmune diseases like rheumatoid arthritis are multifactorial in nature, requiring both genetic and environmental factors for onset. Increased predisposition of females to a wide range of autoimmune diseases points to a gender bias in the multifactorial etiology of these disorders. However, the existing evidence to date has not provided any conclusive mechanism of gender-bias beyond the role of hormones and sex chromosomes. The gut microbiome, which impacts the innate and adaptive branches of immunity, not only influences the development of autoimmune disorders but may interact with sex-hormones to modulate disease progression and sex-bias. Here, we review the current information on gender bias in autoimmunity and discuss the potential of microbiome-derived biomarkers to help unravel the complex interplay between genes, environment and hormones in rheumatoid arthritis.

Gas chromatography-mass spectrometry of biofluids and extracts

Gas chromatography-mass spectrometry (GC-MS) has been widely used in metabonomics analyses of biofluid samples. Biofluids provide a wealth of information about the metabolism of the whole body and from multiple regions of the body that can be used to study general health status and organ function. Blood serum and blood plasma, for example, can provide a comprehensive picture of the whole body, while urine can be used to monitor the function of the kidneys, and cerebrospinal fluid (CSF) will provide information about the status of the brain and central nervous system (CNS). Different methods have been developed for the extraction of metabolites from biofluids, these ranging from solvent extracts, acids, heat denaturation, and filtration. These methods vary widely in terms of efficiency of protein removal and in the number of metabolites extracted. Consequently, for all biofluid-based metabonomics studies, it is vital to optimize and standardize all steps of sample preparation, including initial extraction of metabolites. In this chapter, recommendations are made of the optimum experimental conditions for biofluid samples for GC-MS, with a particular focus on blood serum and plasma samples.

Systems level metabolic phenotype of methotrexate administration in the context of non-alcoholic steatohepatitis in the rat

Adverse drug reactions (ADRs) represent a significant clinical challenge with respect to patient morbidity and mortality. We investigated the hepatotoxicity and systems level metabolic phenotype of methotrexate (MTX) in the context of a prevalent liver disease; non-alcoholic steatohepatitis (NASH). A nuclear magnetic resonance spectroscopic-based metabonomic approach was employed to analyze the metabolic consequences of MTX (0, 10, 40, and 100 mg/kg) in the urine and liver of healthy rats (control diet) and in a model of NASH (methionine-choline deficient diet). Histopathological analysis confirmed baseline (0 mg/kg) liver necrosis, liver inflammation, and lipid accumulation in the NASH model. Administration of MTX (40 and 100 mg/kg) led to liver necrosis in the control cohort, whereas the NASH cohort also displayed biliary hyperplasia and liver fibrosis (100 mg/kg), providing evidence of the synergistic effect of MTX and NASH. The complementary hepatic and urinary metabolic phenotypes of the NASH model, at baseline, revealed perturbation of multiple metabolites associated with oxidative and energetic stress, and folate homeostasis. Administration of MTX in both diet cohorts showed dose-dependent metabolic consequences affecting gut microbial, energy, nucleobase, nucleoside, and folate metabolism. Furthermore, a unique panel of metabolic changes reflective of the synergistic effect of MTX and NASH was identified, including the elevation of hepatic phenylalanine, urocanate, acetate, and both urinary and hepatic formiminoglutamic acid. This systems level metabonomic analysis of the hepatotoxicity of MTX in the context of NASH provided novel mechanistic insight of potential wider clinical relevance for further understanding the role of liver pathology as a risk factor for ADRs.