Serum Metabolic Signatures of Four Types of Human Arthritis - Journal

Article pubs.acs.org/jpr

Serum Metabolic Signatures of Four Types of Human Arthritis Miao Jiang,†,‡ Tianlu Chen,‡,§ Hui Feng,∥ Yinan Zhang,§ Li Li,† Aihua Zhao,§ Xuyan Niu,† Fei Liang,† Minzhi Wang,† Junping Zhan,† Cheng Lu,† Xiaojuan He,† Lianbo Xiao,*,∥ Wei Jia,§,*,⊥ and Aiping Lu*,†,# †

Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China Center for Translational Medicine, and Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200233, China ∥ Guanghua Integrative Medicine Hospital & Institute of Arthritis Research, Shanghai Academy of Chinese Medical Sciences, Shanghai 200052, China ⊥ University of Hawaii Cancer Center, Honolulu, Hawaii 96813, United States # Hong Kong Baptist University School of Chinese Medicine, Kowloon, Hong Kong §

S Supporting Information *

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

KEYWORDS: metabonomics, rheumatoid arthritis, osteoarthritis, ankylosing spondylitis, gouty arthritis

1. INTRODUCTION

rheumatoid arthritis (RA), osteoarthritis (OA), ankylosing spondylitis (AS), gout, lupus, and psoriatic arthritis.6 Arthritic diseases are common worldwide. Recent epidemiological studies reported in 2010 indicated that the prevalence of

Arthritic diseases are characterized by inflammation and loss of function in the joints and connective tissue, associated with significant morbidity and mortality.1−3 Currently, there are more than 100 varieties of arthritic diseases, and they collectively become a major health and financial burden for societies.4,5 The most common arthritic diseases include © 2013 American Chemical Society

Received: May 2, 2013 Published: July 2, 2013 3769

dx.doi.org/10.1021/pr400415a | J. Proteome Res. 2013, 12, 3769−3779

Journal of Proteome Research

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suggesting that the differential diagnosis of arthritic conditions is possible with a metabonomics approach.35 In summary, biomarker discovery using a metabonomics approach represents a novel strategy emerging in clinical research for the diagnosis, patients stratification, assessment of severity and drug response in different pathophysiological states of the arthritic conditions. In our previous study, metabolic features of RA were successfully characterized using integrated liquid chromatography−mass spectrometry (LC−MS) and gas chromatography−mass spectrometry (GC−MS) metabonomics analysis, and the two metabolic phenotypes were differentiated and biologically interpreted.23,36 In OA patients, a distinct metabolites profile was identified relative to the healthy controls and significantly altered histamine metabolism was detected in association with knee effusion symptoms.31 An interesting observation is that several different arthritic diseases share a number of dysregulated metabolic pathways, such as TCA cycle, amino acid, purine and lipid metabolism.17,23,24,29−32 However, there is no single study conducted to compare the serum or plasma metabolic profiles of several different arthritic diseases to characterize the common and differential metabolic defects associated with different types of arthritis. In the present study, we report a mass spectrometry-based metabolic phenotyping study to identify the global metabolic defects as well as distinct metabolic signatures of four major types of arthritisRA, OA, AS, and goutin comparison with healthy control subjects. The study is aimed to identify the global metabolic profiles of all arthritic patients with common metabolic features presumably resulting from joint inflammation and lesion. We also aim to further characterize metabolic signatures of individual types of arthritis and potential biomarkers for phenotypic differentiation.

the arthritic diseases worldwide ranges from 11.6 to 46.4% in the past two decades (varying by geographical locations, study protocols, and ages of the people surveyed).1,7 For example, in the corresponding 2005 US population from the Census Bureau, about 46.4 million US adults have self-reported to have doctor-diagnosed arthritis,8 and 26.9 million Americans aged 25 or older had clinical OA of some joints.9 In 2005, hospitalization for musculoskeletal procedures in the USA involving predominantly knee arthroplasties and hip replacements was estimated to cost $31.5 billion or more than 10% of all hospital care,10 whereas only 10 years earlier the entire cost of OA in the USA was estimated at $15.5 billion.1 Early diagnosis and treatment of arthritic disease is paramount in preventing joint damage and disability. Even though the importance of early intervention has been well recognized for a long time,11 the diagnostic criteria for many diseases such as RA at an early stage may not be appropriate or are difficult to apply, leading to delays in the use of appropriate treatments.12,13 For example, the mean time for the clinical diagnosis of AS is 5−10 years after disease onset, despite the fact that effective and potentially disease-modifying treatments such as tumor necrosis factor (TNF) inhibitors are available to improve the mobility and quality of life of AS patients.14 Arthritic diseases appear to be influenced by both genetic and environmental factors. On one hand, various arthritic diseases share many clinical manifestations and show similar laboratory parameters, leading to a great difficulty and confusion in diagnosis.11 On the other hand, there is strong evidence that patients with arthritic diseases are at a high risk for developing comorbid disorders,1,15 which may tend to show atypical clinical features and thus become difficult to diagnose and differentiate from other arthritic conditions. Some of the traditional medical systems, such as traditional Chinese medicine (TCM), take a different approach in diagnosing and treating these arthritic diseases. In TCM, patients who demonstrate similar symptoms, regardless of the diseases, are grouped together and treated with similar therapies with satisfactory results.16−18 Taken together, these challenges underscore the complexity of the arthritic diseases and highlight the important role of translational research in the discovery of new biomarkers for these complex conditions. Metabolic profiling represents a new approach that delineates a large panel of metabolic parameters and thus allows a global and potentially more personalized diagnostic means to be used in combination with conventional protocols.19 The rationale for the application of clinical metabonomics is that perturbations in a biological system, for instance, those caused by a disease, are identifiable as changes in concentrations of certain metabolites. By using multivariate statistics, it is possible to describe patterns of metabolite markers that are highly discriminatory for the perturbation and/or a disease state.20,21 To date, metabolic profiling has been used to identify biomarkers for several arthritic diseases,22 including RA,4,17,23−28 AS,29 OA,30,31 and gout.32 Results from these biomarker studies suggest that a panel of markers rather than a single compound may be promising for an accurate diagnosis of these diseases.33,34 An additional advantage of evaluating patients using a metabonomic strategy is the possibility of revealing underlying biochemical phenomena associated with the disease, thus providing insights that help the development of a better understanding of the disease state.29 A synovial fluid metabolic signature of septic arthritis was distinct from the other samples including crystal-associated arthritis, RA or spondylarthritis,

2. MATERIAL AND METHODS 2.1. Clinical Samples

A total of 114 patients diagnosed with one of the four types of arthritis were enrolled in this study from three hospitals (China-Japan Friendship Hospital in Beijing, Shanghai Guanghua Rheumatic Hospital and the First Hospital affiliated to Anhui University of Chinese medicine). Samples from four types of arthritis were collected at each hospital following the same protocol. There were 27 female RA patients, aged 40−68 years old; 27 female OA patients, aged 39−73 years old; 27 male AS patients, aged 18−55 years old; and 33 male gout patients (GA), aged 30−69 years old. All patients were diagnosed with active and mild or moderate arthritic conditions. Patients with comorbid disorders were excluded. The clinical diagnosis and blood examination reports of all patients were obtained from the hospitals. The 60 healthy volunteers (30 male and 30 female, 25−74 years old), were selected by a routine physical examination and any subjects with inflammatory conditions or other articular disorders were excluded. Body mass index (BMI), erythrocyte sedimentation rate (ESR) and C Reactive Protein (CRP) levels for each arthritic patient were also assessed. A dietary questionnaire was completed before blood sample collection, which recorded the whole diet information and dietary habits and was used to exclude the patients with different diet habits such as alcohol comsumption or complete vegetable diet. Clinical information on participants is provided in Table 1. Venous blood was 3770



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Table 1. Demographic and Clinical Chemistry Characteristics of 174 Human Subjects group and abbrev C

RA OA AS GA

-all Control-male Control-female Rheumatoid Arthritis Osteoarthritis Ankylosing Spondylitis Gouty Arthritis

number

age mean [min,max] [25,74] [23,55] [36,74] [40,68]

gender female/male

BMI (kg/m2) mean [min,max] ± SD

ESR mean (mm/h)

C reactive protein mean ± SD (mg/L)

30:30 male female 27:0

22.10 [16.41, 29.14] ± 3.45

67.17 ± 27.74

36.03 ± 65.60

60 30 30 27

34 33 47 53

27 27

58 [39,73] 31 [18,55]

27:0 0:27

24.36 [20.03, 30.76] ± 2.26 21.95 [16.23, 27.78] ± 2.59

26.30 ± 23.31 34.72 ± 26.19

5.53 ± 8.69 13.86 ± 23.91

33

51 [30,69]

0:33

25.92 [18.92, 31.64] ± 2.54

24.59 ± 27.89

18.95 ± 25.21

mixture of water, methanol and acetonitrile (1:2:7) to 100 μL of serum. After vortexing for 2 min and ultrasonication-assisted extracting for 1 min, the mixture was centrifuged at 12000 rpm for 10 min. The supernatant was transferred into the sampling vial pending UPLC−QTOF MS analysis. A 5 μL aliquot of sample was injected at the “control-arthritis-control” order into a 100 mm × 2.1 mm, 1.7 μm BEH C18 column (Waters, Milford, MA) held at 40 °C using an ultra performance liquid chromatography system (Waters, Milford, MA). One QC sample was run after each 10 serum samples. All the samples were kept at 4 °C during the analysis. The column was eluted with a linear gradient of 1−20% B over 0−1 min, 20−70% B over 1−3 min, 70−85% B over 3−8 min, 85−100% B over 8−9 min, the composition was held at 100% B for 1 min. For positive ion mode (ES+) where A = water with 0.1% formic acid and B = acetonitrile with 0.1% formic acid, while A = water and B = acetonitrile for negative ion mode (ES−). The flow rate was 0.4 mL/min. The mass spectrometric data were collected using a Waters Q-TOF premier (Manchester, UK) equipped with an electrospray source operating in either positive or negative ion mode. The source temperature was set at 120 °C with a cone gas flow of 50 L/h, a desolvation gas temperature of 350 °C with a desolvation gas flow of 650 L/h. In the case of positive and negative ion mode the capillary voltage was set to 3.2 and 3 kV, and the cone voltage was 35 and 50 V, respectively. Centroid data were collected from 50 to 1000 m/z with a scan time of 0.3 s and interscan delay of 0.02 s over a 9.5 min analysis time. MassLynx software (Waters) was used for system controlling and data acquisition. Leucine enkephalin was used as the lock mass (m/z 556.2771 in ES+ and 554.2615 in ES−) at a concentration of 100 ng/mL and flow rate of 0.2 mL/min for all analyses.

collected in the morning before breakfast from all the participants at the three hospitals. Sera were prepared within 1 h after blood collection and kept at −80 °C until analysis. The protocol was approved by the Review Board in Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, and all participants gave informed consent before they were involved in the study. 2.2. GC−TOF MS Spectral Acquisition of Serum Samples and Data Pretreatment

Serum metabolites were chemically derivatized following our previously published procedure.37,38 A 100-μL aliquot of serum sample was spiked with two internal standard solutions (10 μL of L-2-chlorophenylalanine in water, 0.3 mg/mL; 10 μL of heptadecanoic acid in methanol, 1 mg/mL) and vortexed for 10 s. The mixed solution was extracted with 300 μL of methanol/ chloroform (3:1) and vortexed for 30 s. After storing for 10 min at −20 °C, the samples were centrifuged at 12000× g for 10 min. An aliquot of the 300-μL supernatant was transferred to a glass sampling vial to vacuum-dry at room temperature. The residue was derivatized using a two-step procedure: 80 μL of methoxyamine (15 mg/mL in pyridine) at 30 °C for 90 min and 80 μL of BSTFA (1% TMCS) at 70 °C for 60 min. Each 1μL aliquot of the derivatized solution was injected in splitless mode into an Agilent 6890N gas chromatograph coupled with a Pegasus HT time-of-flight mass spectrometer (Leco Corporation, St. Joseph, MI). The samples were run in the order of “1 quality control (QC) − 10 samples (controls and diseases) − 1 quality control”, alternately, to minimize systematic deviations. The QC samples consisting of multiple reference standards were derivatized using the same procedure as samples. Separationwas achieved on a DB-5 ms capillary column (30 m × 250 μm i.d., 0.25-μm film thickness; (5%-phenyl)methylpolysiloxane bonded and cross-linked; Agilent J&W Scientific, Folsom, CA), with helium as the carrier gas at a constant flow rate of 1.0 mL/min. The temperature of injection, transfer interface, and ion source were set to 270, 260, and 200 °C, respectively. The GC oven temperature was set to 2 min isothermal heating at 80 °C, followed by 10 °C/ min ramps to 180 °C, 6 °C/min to 230 °C, and 40 °C/min to 295 °C, and a final 8 min maintenance at 295 °C. Electron impact ionization (70 eV) at full scan mode (m/z 30−600) was used, with an acquisition rate of 20 spectra/s in the TOF MS setting.

2.4. GC−TOF MS and UPLC−QTOF MS Data Analysis

The acquired data from GC−TOF MS analysis were exported in NetCDF and excel format by ChromaTOF software (v3.30, Leco Co., CA). CDF and csv files were extracted using Matlab (MATLAB 7.0, The MathWorks, Inc.) scripts hierarchical multivariate curve resolution (H-MCR) and Peak integration tool (PIT) for data pretreatment such as baseline correction, denoising, smoothing, alignment, time-window splitting, multivariate curve resolution, and peak integration.39 The internal standard was used for data quality control (reproducibility) and data normalization. Compound identification was performed by comparing the mass fragments with NIST 11 Standard mass spectral databases in NIST MS search 2.0 (NIST, Gaithersburg, MD) software with a similarity of more than 70% and in house reference compounds (∼800 mammalian metabolite standards). The resulting three-dimensional data set includes sample

2.3. UPLC−QTOF MS Spectral Acquisition of Serum Samples and Data Pretreatment

Each 100 μL serum was used for metabolite extraction prior to UPLC−QTOF MS analysis. The metabolite extraction procedure was carried out after adding 20 μL 0.3 mg/mL L2-chlorophenylalanine as the internal standard and 500 μL of a 3771



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Figure 1. OPLSDA models. OPLSDA models of the arthritis with corresponding values of R2X, R2Y, and Q2. (a) Scores plot of control (healthy volunteers, blue square) vs arthritic patients (red circle) obtained from serum samples; (b) scores plot of control (blue square) vs RA (red circle) vs OA (black triangle); (c) scores plot of control (blue square) vs AS (red circle) vs GA (black triangle).

information and the retention time and intensities of all the identified metabolites. Internal standards and any known artificial peaks, such as peaks caused by noise, column bleed and BSTFA derivatization procedure, were removed. The UPLC−QTOF MS ES+ and ES− raw data were analyzed by the MarkerLynx Applications Manager version 4.1 (Waters, Manchester, U.K.) using parameters reported in our previous work.40Metabolites obtained from POS and NEG mode of UPLC−QTOF MS analysis were identified with the aid of available reference standards in our lab and the webbased resources such as the Human Metabolome Database (http://www.hmdb.ca/). The resulting two three-dimensional data sets contain sample information and the ion intensity and retention time-m/z pairs of all the identified metabolites. The data was then normalized by corresponding internal standard. To obtain consistent differential variables, the resulting matrix was further reduced by removing any peaks with missing value (ion intensity = 0) in more than 80% of the samples. The ion peaks generated by the internal standard were also removed. Compound was annotated by means of available reference standards in our lab (by comparing the accurate mass and retention time) as well as the web-based resources such as the Human Metabolome Database (http://www.hmdb.ca/). The three data sets resulting from GC−TOF MS, UPLC− QTOF MS ES+, and ES− (expressed as G, P, and N, respectively) were mean centered, unit variance scaled and combined before uni- and multivariate statistical analysis in the SIMCA-p 12.0 Software package (Umetrics, Umeå, Sweden), SPSS (v19, IBM, New York, NY), and Matlab. Missing values ( 1) in the above-mentioned OPLS-DA model as well as the Student’s t test (p < 0.05) were selected and their variations are summarized in SI Table 1. Among these metabolites, 52 were validated by reference standards.

3.3. Metabolite Markers Identified between RA and OA, for Classification of the Two Conditions

Among the 175 metabolites, 13 representative metabolites were selected as a panel of candidate markers differentiating RA from OA patients (all female). The selection was done by two steps, (1) rank the separating capacity (in descending order) of the annotated metabolites by their VIP, area under ROC, and pvalue (t test) respectively and three lists were built, and (2) select variables fall in the first 10% (top 17) of all the lists. Heat-map of all the 13 differential metabolites (alanine, tryptophan, 5-oxoproline, sarcosine, tyrosine, threonine, citric acid, lysine, acetylornithine, histamine, 24-hydroxycalcitriol, cisaconitic acid and pyroglutamic acid) and the scatter plots of some typical metabolite markers are shown in Figure 3 to visualize the fluctuations (in fold change) of these markers.

3.2. Differentially Expressed Serum Metabolites Shared by Four Types of Arthritis, Indicative of a Common Metabolic Defects Resulting from Joint Lesion and Inflammation

Among the annotated metabolites, homoserine, 4,8-dimethylnonanoyl carnitine, glyceraldehyde, lactic acid, dihydroxyfumaric acid and aspartic acidwere identified as candidate markers shared by four types of arthritis compared with healthy controls. As presented in Figure 2a, all of them except 4,83773


Dihydroxyfumaric acid3Nf Aspartic acid1G

Lactic acid

1G

Glyceraldehyde1N

4,8-Dimethylnonanoyl carnitine3Pe

Homoserine

Logistic regression was used to combine the 13 variables into one variable. The ROC curve based on this variable yielded satisfactory AUC of 0.86 with a sensitivity of 85% and specificity of 81%.

a Superscripts: 1, identified by standard; 2, identified by NIST05 library; 3, identified by HMDB library; G, GC−TOF MS platform; P, UPLC−QTOF MS platform positive ion mode; N, UPLC−QTOF MS platform negative ion mode. bFold change of two groups (mean ratio). cp-value of t-test. dVIP value of OPLSDA model. e4,8-Dimethylnonanoyl carnitine: HMDB02050. fDihydroxyfumaric acid: HMDB06202.

2.27 2.34 3.28 × 10−04 2.14 × 10−04 1.44 1.89 3 1.89 3.36 × 10−07 2.89 × 10−03 1.46 1.74 3.32 2.21 8.76 × 10−11 1.22 × 10−04 1.77 1.95 2.7 2.37 2.12 × 10−07 1.07 × 10−05 1.85 1.98 3.53 2.68 1.68 × 10−14 1.93 × 10−08 1.62 1.9

3.1 2.42 × 10 1.7 2.44 7.15 × 10 1.57 3.51 1.33 × 10 2.15 2.86 2.15 × 10 2.04 3.84 1.82 × 10

2.15

−07 −05 −12 −08

7.43 × 10−04 3.15 3.48

1.78

7.01 × 10−08

2.78

1.73

1.96 × 10−09

1.49

1.82 × 10−07

3.05

1.43

1.39 3.38 × 10−02 0.41 1.69 8.25 × 10−03 0.19 1.31 2.94 × 10−02 0.29 1.55 6.34 × 10−03 0.13

4.16 × 10−14

−17

1.84

1.2

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Glycine, serine and threonine metabolism Fatty acid metabolism Fatty acid metabolism Glycolysis metabolism TCA cycle Urea cycle

1.6

4.88 × 10

p FC pathways

2.27

1.2 2 2.92 × 10 1.27 2.42

p FC VIP

−07 1P

compounds

2.90 × 10−06

3.21 × 10

p FC

−04

VIP

d c

control vs RA

b d

control vs arthritics

c b a

Table 2. Statistical Analysis of Representative Differential Metabolites

0.26

1.84 1.17 1.61 1.17 × 10 1.15 2.09

FC

−04

VIP

d c b

control vs OA

4.45 × 10

−03

pc

−02

p

c b

control vs AS

VIP

d

FC

b

control vs GA

VIPd


3.4. Metabolite Markers Identified between SA and GA, for Classification of the Two Conditions

Similarly, 16 representative metabolites were identified as a panel of candidate markers based on their high fold changes, AUC, and VIP values between AS and GA patients (all males). As presented in Figure 4, differentially expressed metabolites in AS and GA include dihydrothymine, alloxanoic acid, uric acid, 5-oxoproline, valine, creatine, arabitol, succinic acid, taurine, sucrose, lysine, citrulline, sarcrosine, valine, malic acid, alanine and cysteine. A heat-map of all the differential metabolites and the scatter plots of some typical metabolite markers are shown in Figure 4 to visualize their fluctuations (in fold change). Logistic regression was used to combine the 16 variables into one variable. As expected, the ROC curves based on this variable yielded satisfactory results (Figure 4b). The area under the curve (AUC) reached 0.88 with a sensitivity of 79% and specificity of 85%.

4. DISCUSSION 4.1. Common Metabolic Features Resulting from Joint Inflammation and Lesion of All Arthritic Patients

The global metabolic profile suggests that there are common metabolic defects resulting from joint inflammation and lesion. Among the six identified biomarkers, five were elevated in serum of patients with arthritis and one was decreased, which is consistent with previous biochemical studies of biofluid from arthritic animals and humans with inflammatory arthritis.26,41,42 The related pathway of each biomarker was searched in the KEGG PATHWAY Database (http://www.genome.jp/kegg/), Human Metabolome Database Version 3.0 (http://www.hmdb. ca/) and ChEBI Database (http://www.ebi.ac.uk/chebi/init. do). The six biomarkers distributed in five pathways and the interaction of these pathways is illustrated in Figure 5. Except for 4,8-dimethylnonanoyl carnitine, the other five biomarkers participate in a metabolic network including the pathways of Glycolysis metabolism, TCA cycle, Glycine, Fatty acid metabolism, Serine and threonine metabolism and Urea cycle. From Figure 5 we can see that the pyruvate to lactate pathway is the key biological process in this network and energy metabolism is disturbed after the onset of arthritic inflammation. The other four elevated metabolites are all correlated with this pathway. Our data further suggest lactate as a marker of arthritic inflammation. Lactate has been detected in many chronic inflammatory conditions such as in inflamed joints, and the acidosis associated with increasing lactate concentrations is thought to play a pathogenic role in cell transformation and autoantigen development in some inflammatory environments.17 Lactate is a metabolic product of pyruvate, which was found to be elevated in the synovial fluid of patients with RA.43 Similarly, pyruvate has also been found to be an biomarker of inflammatory arthritis and proposed as a noninvasive imaging biomarker for detecting autoimmune disorders (including inflammatory arthritis) and monitoring their treatments.43,44 Pyruvate occupies a key intersection in several metabolic pathways as an intermediatein energy metabolism in living cells. Pyruvate levels are elevated in joints affected by OA,23 and inhibitors of pyruvate metabolism down3774



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Figure 3. RA vs OA. (a) Heat-map of fold change of 13 differential metabolites; (b) ROC of diagnosis panel by 13 differential metabolites; (c) Scatter plot of Sarcosine; (d) Scatter plot of 5-Oxoproline; (e) Scatter plot of Alanine; (f) Scatter plot of Tryptophan.

Figure 4. AS vs GA. (a) Heat-map of fold change of 16 differential metabolites; (b) ROC of diagnosis panel by 16 differential metabolites; (c) Scatter plot of Uric acid; (d) Scatter plot of Succinic acid; (e) Scatter plot of Arabitol; (f) Scatter plot of Valine; (g) Scatter plot of Creatine; (h) Scatter plot of 5-Oxoproline.

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Figure 5. Network of six identified biomarkers according to pathway analysis. The yellow dashed area denotes the identified metabolites. Yellow dashed area denotes the identified metabolites. Up arrows demonstrate up-regulated and down arrows mean down-regulated. Red square indicates the key metabolic pathways in the network.

regulate inflammation.45 However, little attention was paid to study its role in other inflammatory arthritis such as AS and GA. An important feature of the rheumatoid joint is hypoxia,46 which is known to be prevalent in the inflammatory environments such as those associated with wounds, malignant tumors, bacterial infections and autoimmunity.35 Increasing hypoxia in the inflammatory site is associated with poorer disease outcome such as increased macroscopic synovitis in RA.47 Reduction in environmental oxygen leads to the stabilization of a transcription factor, hypoxia-inducible factor (HIF). Hypoxia and HIF has a large effect on cellular metabolism. HIF prefers glycolytic metabolism over oxidative phosphorylation by inducing the expression of glycolytic enzymes.48 This allows ATP generation to continue in the absence of sufficient oxygen albeit at a much reduced efficiency per molecule of glucose. It also induces the upregulation of lactate dehydrogenase A, therefore promoting the conversion of pyruvate (produced during glycolysis) to lactate.23 Exposure of macrophages to hypoxic conditions is associated with upregulation of a whole gamut of proinflammatory cytokines such as IL-1,49 IL-6,24 IFN-γ50 and TNF-α.19 Both low oxygen levels and their downstream effects, such as lactate production, may give rise to this macrophage phenotype. Such phenotypic changes observed in response to the hypoxic conditions of the inflammatory site suggest an important role of small molecule metabolites in regulation of immune cells. The detection of lactate in metabonomics studies suggests that this may be a common inflammatory component in the 4 types of inflammatory arthritis including AS and GA, besides RA and OA.

The only biomarker detected at a significantly decreased level is 4,8-dimethylnonanoyl carnitine, which is tentatively determined using the HMDB with the exact molecular weight. It is an intermediate in phytanic and pristanic acid metabolism. Both phytanic acid and pristanic acid are initially oxidized in peroxisomes to 4,8-dimethylnonanoyl-CoA, which is then converted to 4,8-dimethylnonanoyl carnitine, and exported to the mitochondrion. Phytanic acid is first degraded by alphaoxidation, yielding pristanic acid, which is subsequently degraded by beta-oxidation. This bioprocess occurs in peroxisome. Abnormality of phytanic acid and pristanic acid is found in blood and tissues of patients affected with generalized peroxisomal disorders.51 Therefore, the depleted 4,8-dimethylnonanoyl carnitine should be the consequence of the down-regulated peroxisome function and elevated hypoxia, which had been detected in a wide variety of joint diseases and appeared to be nonspecific.35 In summary, hypoxia and the subsequent perturbed energy metabolism is an unique biological process commonly observed in the 4 types of arthritis. The pyruvate to lactate pathway might play a key role in the diseases development and increased conversion of pyruvate into lactate may be indicative of the presence of arthritic inflammation and disrupted peroxisomal lipid metabolism. 4.2. Characteristic Metabolic Signatures of Individual Types of Arthritis and Potential Biomarkers for Phenotypic Differentiation

Differential metabolites of individual types of arthritis were also detected in our study. After comparing to healthy control samples, the characteristic metabolites of each disease were identified by comparing RA with OA (all female), and AS with 3776



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tryptophanhave been identified as significant contributing variables to GA classification.32,56,57 As metabolome is close to the biochemical functions of a cell or organism, the metabonomics will be the most relevant to understanding phenotypes and biological functioning.32 The data in this study allow us to monitor the diverse and specific metabolic changes in RA, OA, AS and GA in a holistic manner. Furthermore, these results provide us with a potential way to distinguish the different forms of arthritis to ensure an early and accurate diagnosis using a panel of metabolite markers. Such a strategy, considering multiple metabolite markers as a whole instead of a stand-alone single marker for diagnosis, is very similar to the TCM approach, which may help us to develop novel theranostic imaging methods in addition to existing clinical modalities.

GA (all male), respectively. A panel of 13 metabolites was identified between RA and OA, with an AUC in a ROC model of 0.86. Similarly, 16 metabolites were detected between AS and GA for differentiating the two conditions with an AUC of 0.88. In clinical practice, arthritis, especially early inflammatory arthritis, has been difficult to define or differentiate between various types of early arthritis.52 Generally, patients with early arthritis refer to those with the potential for development of persistent inflammatory arthritis but without a recognizable clinical pattern.52 These patients might progress into other rheumatic conditions.53 From the clinical viewpoint, therapeutic outcomes would be improved if effective therapy could be used prior to the development of irreversible damage. Therefore, it is of critical value to ensure accurate diagnosis for patients with early arthritis. Our findings in this study provide a potentially important complement to the current diagnostic studies in discriminating between different forms of early arthritis, thereby improving the therapeutic decisionmaking and, thus, outcomes. RA and OA share some markers; the two conditions also possess disease-specific markers. Most common metabolites shared by RA and OA are correlated with ATP, ADP, TNF, IL6, LDL, phosphate, Akt, and FOS. All these molecules or cytokines play important roles in the process of arthritic diseases. The two profiles suggest that there are common pathways disturbed in RA and OA, such as TCA cycle, fatty acid metabolism, histidine metabolism and Amino Acid metabolism, the characteristics of metabolic signatures of the two conditions are distinctly different. Given the dysfunction reflected by abnormalities of these metabolites, the different aggravation of the two diseases might be better understood. For example, Histamine is an important modulator of numerous physiological processes including inflammation,54 contributing to the articular cartilage degenerative or catabolic behavior in vitro by stimulating the proliferation of OA human articular chondrocytes (HAC).55 It has been found to be a potential biomarkers for OA, and the increase of urinary histamine is believed to be a possible consequence of increased Histidine decarboxylase activity in OA.36 In addition, Histidine metabolism disorders are often accompanied with knee effusion symptoms. Lower level of histidine and higher level of histamine in OA patients were found with knee effusion as compared to OA patients without the same symptom.36 Our data suggests that a panel of multiple metabolites could be used to discriminate RA from OA, especially when patients suffer from less severe symptoms or at an early arthritis stage. These results also provide important clues for the future studies further exploring the pathogenesis of different forms of arthritis. Similarly, most shared metabolites in AS and GA are highly correlated with ATP, NOS2, NOS3, hydrogen peroxide, phosphate, IGF1, ERK1/2, Akt, D-glucose and L-glutamic acid, reflecting their active roles in innate immunity and the acute inflammatory response. Our data thus strongly support the roles of biochemical changes at metabolic level in an innate inflammatory pathogenesis in AS and GA, and identify the common and the differential pathways between the two conditions. The altered metabolites in AS involve carbohydrate, amino acids, organic acid, phospholipids and small peptides; and in GA showing clearly dysfunctions of protein, purine and glucose metabolism. These findings are consistent with previous studies, for example, serum uric acid, creatinine and

4.3. Study Limitations

However, several potential limitations in our study should not be neglected. First, our study population is relatively small in each group, the study of larger samples is needed in the future. Second, some biases exist in samples due to the gender differences among groups which may confound our results, although we have carefully grouped the samples for the comparative analysis so that they are age- and gender-matched and the score plots of our metabolic profiles demonstrate excellent discriminatory power. In addition, the information of medication history of the patients, which may also influence the results of metabolic profiling, is available but not interpreted in our results.

5. CONCLUSIONS In conclusion, a global metabolic profile is identified from all arthritic patients suggesting that there are common metabolic defects resulting from joint inflammation and lesion, elevated energy metabolism and pyruvate to lactate pathway in the 4 types of arthritis. On the other hand, differentially expressed serum metabolites constituting a metabolic signature of each type of arthritis are potential biomarkers for disease monitoring and personalized medication complementary to the existing clinical modalities.

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ASSOCIATED CONTENT

S Supporting Information *

SI Figures 1−4 and SI Table 1. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Lianbo Xiao, Phone +86 13701888178, Fax 02162809946, Email [email protected]. Wei Jia, Phone 808-564-5823, Email [email protected]. Aiping Lu, Phone +861064067611, Fax + 861084032881; E-mail [email protected]. Author Contributions ‡

These authors contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Supported in part by the projects from the National Natural Science Foundation of China (Grant no. 30825047 and no. 30902003), by the projects from China Academy of Chinese 3777



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Medical Sciences (no. Z0134), and by Drug Innovation Program of National Science and Technology Project (no. 2011ZX09307-001-02).

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ABBREVIATIONS RA, rheumatoid arthritis; GA, gouty arthritis; OA, osteoarthritis; AS, ankylosing spondylitis; GC−TOF MS, gas chromatography coupled with time-of-flight mass spectrometry; UPLC−QTOF MS, ultraperformance liquid chromatography quadrupole-time-of-flight mass spectrometry; TCM, traditional Chinese medicine; BMI, Body mass index; ESR, erythrocyte sedimentation rate; CRP, C Reactive Protein; TNF, tumor necrosis factor; QC, quality control; VIP, variable importance in the projection; PCA, Principal component analysis; OPLSDA, orthogonal projection to latent structures discriminant analysis; Akt, v-akt murine thymoma viral oncogene homologue (also known as Protein Kinase B (PKB), is a serine/threoninespecific protein kinase); FOS, FBJ murine osteosarcoma viral oncogene homologue; NOS2, nitric oxide synthase 2; NOS3, nitric oxide synthase 3; IGF1, Insulin-like growth factor 1; ERK1/2, Extracellular Regulated Protein Kinases 1/2

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