Article pubs.acs.org/jpr

Urinary Proteome Profile Predictive of Disease Activity in Rheumatoid Arthritis Min Jueng Kang,†,○ Yune-Jung Park,‡,○ Sungyong You,§,○ Seung-Ah Yoo,∥ Susanna Choi,∥ Dong-Ho Kim,∥ Chul-Soo Cho,∥,⊥ Eugene C. Yi,*,† Daehee Hwang,*,§,# and Wan-Uk Kim*,∥,△ †

Department of Molecular Medicine and Biopharmaceutical Sciences, School of Convergence Science and Technology and College of Medicine or College of Pharmacy, Seoul National University, Seoul 110-799, Korea ‡ Division of Rheumatology, Department of Internal Medicine, The Catholic University of Korea, St. Vincent’s Hospital, Suwon 110-150, Korea § School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Gyeongbuk 790-784, Korea ∥ Research Institute of Immunobiology, Catholic Research Institute of Medical Science, Seoul 151-000, Korea ⊥ Division of Rheumatology, Department of Internal Medicine, The Catholic University of Korea, Yeouido St. Mary’s Hospital, Seoul 151-000, Korea # Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu, 711-873, Korea △ Division of Rheumatology, Department of Internal Medicine, The Catholic University of Korea, St. Mary’s Hospital, Seoul 137-701, Korea S Supporting Information *

ABSTRACT: Current serum biomarkers for rheumatoid arthritis (RA) are not highly sensitive or specific to changes of disease activities. Thus, other complementary biomarkers have been needed to improve assessment of RA activities. In many diseases, urine has been studied as a window to provide complementary information to serum measures. Here, we conducted quantitative urinary proteome profiling using liquid chromatography−tandem mass spectrometry (LC−MS/MS) and identified 134 differentially expressed proteins (DEPs) between RA and osteoarthritis (OA) urine samples. By integrating the DEPs with gene expression profiles in joints and mononuclear cells, we initially selected 12 biomarker candidates related to joint pathology and then tested their altered expression in independent RA and OA samples using enzyme-linked immunosorbent assay. Of the initial candidates, we selected four DEPs as final candidates that were abundant in RA patients and consistent with those observed in LC−MS/MS analysis. Among them, we further focused on urinary soluble CD14 (sCD14) and examined its diagnostic value and association with disease activity. Urinary sCD14 had a diagnostic value comparable to conventional serum measures and an even higher predictive power for disease activity when combined with serum C-reactive protein. Thus, our urinary proteome provides a diagnostic window complementary to current serum parameters for the disease activity of RA. KEYWORDS: rheumatoid arthritis, urine, biomarkers, soluble CD14, LC−MS/MS analysis

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progression.2 Notwithstanding these benefits, current serum biomarkers for RA are neither highly sensitive nor specific to changes in disease activities.3 ESR remains elevated for some time after inflammation is reduced by treatments, and it can be influenced by anemia and age. Likewise, CRP is nonspecifically influenced by infection.4 Also, RF is positive only in two-thirds

INTRODUCTION Rheumatoid arthritis (RA), afflicting approximately 1% of the population, is a systemic autoimmune disease primarily affecting joints.1 The accurate assessment of the autoimmune status or disease activity of RA is essential to optimize treatment options. To this end, several serum biomarkers have been used, including erythrocyte sedimentation rate (ESR), Creactive protein (CRP), rheumatoid factor (RF), and anticyclic citrullinated peptide antibody (ACPA). ESR and CRP have been widely used to assess the disease activity of RA. RF and ACPA are representative serologic markers for RA diagnosis and help to predict RA prognosis, including radiographic © 2014 American Chemical Society

Special Issue: Proteomics of Human Diseases: Pathogenesis, Diagnosis, Prognosis, and Treatment Received: January 2, 2014 Published: September 15, 2014 5206

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of RA patients2 and can be falsely detected in non-RA conditions, including pulmonary fibrosis, chronic hepatitis B, and systemic lupus erythematosus (SLE).5,6 Moreover, despite the high specificity between 87.8% and 96.4% of ACPA in RA diagnosis,7 ACPA shows a similar level of sensitivity (60−70%) to RF.8,9 Furthermore, RF and ACPA did not usually correlate with disease activity of RA.2 Therefore, other biomarkers complementary to these conventional serum measures have been needed to improve monitoring of RA activities. For many diseases, urine has been studied as a window to provide complementary information to serum measures. Urine, which can be collected noninvasively and routinely in large quantities, may serve as a potentially rich source of biomarkers reflecting systemic inflammation and renal dysfunction through the excretion of urinary proteins.10 Urinary biomarkers, therefore, have been tested for their ability to assist clinicians in detecting renal damage early in inflammatory diseases with frequent kidney involvement. For example, the proteome profiles of urine samples from SLE patients provided potential biomarkers capable of helping detect preclinical renal damage and monitor renal flares.11 Also, urinary proteome profiles of diabetic mellitus (DM) patients provided potential biomarkers that can be used for early diagnosis of diabetic nephropathy.12 Nevertheless, urinary proteome profiling has not been tested in chronic inflammatory diseases without overt nephropathy. Unlike SLE and DM, it is extremely rare ( 0.95; Materials and Methods). We then quantified the abundances of the 696 proteins in RA and OA samples using the APEX quantitative proteome analysis tool25,33 (Materials and Methods). The analysis of the

Selection and Validation of RA Urine Biomarker Candidates

Among the 296 urinary proteins, we identified 134 DEPs (FDR < 0.05) between RA and OA samples using a previously reported statistical method27 (Figure 1A; Materials and Methods). Interestingly, the 134 DEPs significantly (P < 0.01) represented all the cellular processes represented by the 5209

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Figure 2. Selection of initial biomarker candidates for RA. (A) Enriched cellular processes represented by 134 DEPs in the urine samples of RA patients, compared to OA patients. (B) DEPs involved in the three inflammation-related GOBPs with high enrichment scores. The heat map represents log2-fold-changes of the proteins involved in the three inflammation-related processes (red, up-regulated; green, down-regulated). (C) Relationships between urinary DEPs and the DEGs in joint tissues or PBMCs. P values, calculated by Fisher’s exact test, represent the significance of the overlap in each comparison of the DEPs and DEGs. The table shows the significance of the three inflammation-related processes being enriched by the overlapping DEPs. (D) The 12 initial biomarker candidates that can be secreted to serum based on the HPPP data of the 67 DEPs selected from the integration of the 134 DEPs with gene expression data from joint tissues and PBMCs. Log2-fold-changes of these candidates between RA and OA samples are shown. IGHM, immunoglobulin heavy constant mu; SERPINA3, serpin peptidase inhibitor, clade A (α-1 antiproteinase, antitrypsin), member 3; AZGP1, α-2-glycoprotein 1, zinc-binding; COTL1,coactosin-like 1; HPR, haptoglobin-related protein; SERPINA7, serpin peptidase inhibitor, clade A (α-1 antiproteinase, antitrypsin), member 7; CTSA, cathepsin A; GNS, glucosamine (N-acetyl)-6-sulfatase.

suggesting that the DEPs could be indicative of the deregulation of synovial joints and PBMCs in RA patients. Finally, we integrated the HPPP phase I and II data and subsequently found that 12 of the 67 overlapping DEPs can be secreted into circulation and then excreted into urine based on the HPPP data (Figure 2D and Supporting Information Table S2). Hence, we selected these 12 DEPs as an initial set of urinary biomarker candidates. Next, we evaluated the urinary levels of the initial candidate DEPs using the independent urine samples of 30 RA and 30 OA patients (second cohort; Supporting Information Table S1B) by ELISA. Of the 12 initial candidates, urinary levels of four proteins, GSN, ORM1,ORM2, and sCD14, were significantly higher in RA patients than in OA patients (Figure 3A to D), consistent with the observations in the quantitative proteome profiling. These results suggest their validity as urinary biomarkers to reflect the disease activity of RA. Of the remaining eight candidates, three proteins (haptoglobin-related protein, thyroxin-binding globulin, and zinc-α-2-glycoprotein)

296 urinary proteins (Figure 2A). Of the 134 DEPs, 28 were involved in immune response-related processes (inflammatory response, defense response, and response to wounding). Eight of them were up-regulated, and 20 proteins were downregulated in the RA samples compared to the OA samples (Figure 2B). To select a reliable set of urinary biomarker candidates, we hypothesized that RA urinary biomarkers should reflect molecular changes in joint tissues or PBMCs. To this end, we integrated the DEPs with gene expression profiles in joint tissues [GSE12021,35 GSE1919,36 and GSE7307 (Human Body Index of gene expression) in Gene Expression Omnibus database] and PBMCs [GSE1557337 and GSE1182738] in RA patients, as previously demonstrated39 (Supporting Information Table S2). Of the 134 DEPs, 67 overlapped with the two sets of DEGs identified from the RA joint tissues (P = 1.58 × 10−6) or PBMCs (P = 0.02) (Figure 2C, upper panel). In addition, the 67 overlapping DEPs significantly represented the immune response-related cellular processes (Figure 2C, lower panel), 5210

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respectively. There was no significant difference in urine protein levels of the other five proteins between the independent RA and OA patients (Supporting Information Table S3). Comparison of Urinary sCD14 Levels in RA, OA, and SLE Patients

Among the four candidates confirmed by ELISA, CD14 is known to be involved in the recognition of lipopolysaccharide together with Toll-like receptor 4.40 Moreover, the sCD14 is elevated in the sera of RA patients,41 reflecting disease activity in RA. Therefore, we further focused on sCD14 and sought to evaluate the pathological significance of urinary sCD14 in a larger RA cohort (third cohort; Supporting Information Table S1C), including 274 RA, 120 OA, and 60 SLE patients with no renal involvement. Both GPI-free sCD14 (by proteolytic cleavage) and GPIlinked sCD14 (by protease-mediated shedding) are present in the serum.42 Thus, we examined whether GPI-free or GPIlinked sCD14 were present in the urine. Western blot analysis revealed that the two forms of sCD14 were present in the urine of both RA and OA control patients (Figure 4A). We next measured urinary sCD14 levels by ELISA assays from the urine samples in the large numbers of RA (n = 274), OA (n = 120), and SLE patients (n = 60) (third cohort). The result showed that urinary sCD14 concentrations after adjustment for urine creatinine were higher in RA patients than in SLE and OA patients (Figure 4B). Additionally, there was no difference in urinary sCD14 levels between RA patients treated with certain

Figure 3. Validation of selected initial urinary biomarker candidates. The 12 RA urine biomarker candidates were individually validated in urine samples from the second cohort including 30 RA patients and 30 OA patients by ELISA. Of the 12 initial candidates, 4 proteins, (A) gelsolin (GSN), (B) orosomucoid (ORM) 1, (C) ORM2, and (D) soluble CD14 (sCD14), were shown to be significantly increased in the urine of RA patients, compared to that of OA patients. * P < 0.05. ** P < 0.001.

were undetectable by immunoassays in both RA and OA urine samples; the detection limit was 0.78, 0.78, and 0.63 ng/mL,

Figure 4. Predictive value of urinary sCD14 for the disease activity of RA. (A) Western blot analysis of urinary sCD14. Lane 1, recombinant sCD14 (rCD14); lanes 2 and 3, RA urine samples (RA1−2); lanes 4 and 5, control OA urine samples (Con1−2). (B) Comparison of urinary sCD14 levels in RA, OA, and SLE patients measured by ELISA. (C) Comparison of urinary sCD14 levels in patients with OA (Controls), RA, RA and HBP, and RA and DM. For a comparison between the two groups, the Mann−Whitney U test was used. For a comparison of the three groups, one-way analysis of variance with Bonferroni correction as a post hoc test was performed. (D) Correlation of urinary sCD14 levels with clinical variables measured in RA patients. (E) Comparison of urinary sCD14 levels in low (DAS28 score < 3.2), medium (3.2 ≤ DAS28 score < 5.1), and high (DAS28 score ≥ 5.1) disease activity of RA. A/G, albumin/globulin. *P < 0.05 and **P < 0.01. 5211

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Figure 5. Additional predictive value of urinary sCD14 for the disease activity of RA. (A and B) Positive correlation of urinary sCD14 with serum measures of disease activity, ESR (A) and DAS28 (B), in RA patients with normal CRP. (C) ROC curve analyses of urinary sCD14, CRP, hemoglobin, platelet, and albumin levels for assessing the accuracy in prediction of disease activity. Urinary sCD14 has a high area under curve (AUC = 0.71 [0.63−0.79], P < 0.001) comparable to that of serum CRP (AUC = 0.74 [0.67−0.81], P < 0.001). (D) Predictive power of serum CRP, urinary sCD14, and the ratio of serum CRP to urinary sCD14 (CRP/sCD14) for the disease activity of RA using their optimal cutoff values. (E and F) Predicted probability plot of high disease activity (DAS28 ≥ 5.1) in association with serum CRP levels in RF-negative (E) and RF-positive RA patients (F) with high (◇; >2 SD of urinary sCD14 level in OA patients) versus low (○) urinary sCD14 levels. PPV, positive predictive value; NPV, negative predictive value.

patients had higher urinary sCD14 levels than OA patients, irrespective of the presence of DM or hypertension (Figure 4C). Within the subgroups of RA patients, RA patients with DM showed the highest levels of urinary sCD14, followed by RA patients with hypertension. RA patients with neither DM nor hypertension had the lowest level of urinary sCD14 (Figure

medications, including antirheumatic drugs, and those without (Supporting Information Table S4). After determining that urinary sCD14 is significantly increased in RA, we performed a subgroup analysis according to the presence of hypertension or DM, which may affect urinary protein excretion.43 The results showed that RA 5212

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This analysis revealed that urinary sCD14 had a comparable area under the curve (AUC) of 0.71 to that of serum CRP (Figure 5C). Furthermore, it had a higher sensitivity than CRP in the range of larger than 80% of specificity (1-Specificity ≤ 0.2 in Figure 5C), indicating additional predictive value of urinary sCD14 for disease activity to serum CRP. We then proposed a ratio of serum CRP to urinary sCD14 as a combined measure of serum CRP and urinary sCD14 (Figure 5D) and determined a cutoff value (0.06) of the measure to minimize the sum of false positive and negative error rates in the ROC curve. Sensitivity, specificity, positive predictive value, and negative predictive value of the combined measure were higher than those obtained when either CRP or urinary sCD14 was used alone (Figure 5D). Finally, we tested whether the combined measure (CRP plus urinary sCD14) has a different predictive power of disease activity depending on RF positivity. To this end, we examined the correlation between urinary sCD14 and CRP levels in a subgroup of RF-negative versus positive patients. In RFnegative RA patients with high urinary sCD14, the predicted probability of high activity increased more synergistically with increasing serum CRP than in those with low urinary sCD14 (Figure 5E and F). RF-positive patients also showed a similar result, but such a synergistic power for predicting high disease activity was less prominent in these patients. Collectively, sCD14 may provide an additional dimension complementary to the current CRP in prediction of RA activity, particularly for the RF-negative subgroup.

4C), indicating that both DM and hypertension affect the excretion of urinary sCD14 in RA patients. Correlation of Urinary sCD14 Levels with Disease Activity of RA

To assess the clinical significance of urinary sCD14, we next examined the association of urinary sCD14 with clinical parameters: (1) inflammatory markers of RA (ESR and CRP), (2) disease activity scores (DAS28),44 and (3) parameters related to disease severity of RA (SvdH score, RF positivity, and ACPA positivity). The correlation analyses of the urinary sCD14 levels with the clinical parameters revealed that urinary sCD14 showed positive correlations with ESR, CRP, and DAS28, but a negative correlation with serum albumin (Figure 4D). When we divided RA patients into three groups (low, moderate, and high) based on DAS28, urinary sCD14 levels increased in proportion to DAS28 (Figure 4E). This suggests that urinary sCD14 reflects disease activity of RA. In contrast, urinary sCD14 showed no significant correlation with the parameters associated with disease severity (SvdH score, RF titer, and ACPA titer) (Figure 4D). Furthermore, there was no difference in urinary sCD14 levels between RF-positive patients and RF-negative patients (mean value [interquartile range]: 133.0 [43.1−296.2] ng/mL versus 112.7 [48.9−230.4] ng/mL, P = 0.682) or between ACPA-positive patients and ACPAnegative patients (mean value [interquartile range]: 118.2 [46.3−275.2] ng/mL versus 130.7 [47.5−308.6] ng/mL, P = 0.873). This indicates that urinary sCD14 does not reflect the disease severity of RA.

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DISCUSSION To optimize therapeutic options for RA patients, it is critical to accurately assess disease activity. Several serum biomarkers have been used for this purpose, but they have limitations in their specificities or sensitivities. Thus, it is important to identify alternative biomarkers that can improve the assessment of the disease activity of RA. Urinary biomarkers can provide a complementary window to serum biomarkers. Thus, in this study, we conducted a quantitative proteomic analysis using urine samples of RA and OA patients and then identified 134 DEPs between the RA and OA urine samples. Of the 134 DEPs, we selected 12 proteins as initial biomarker candidates through the integration of the DEPs with gene expression data in joint tissues and PBMCs. Of the initial candidates, we selected four final candidates (GSN, ORM1, ORM2, and sCD14) whose increased levels were confirmed in the urine of independent RA and OA patients by ELISA. The final candidates are associated with RA pathophysiology.47−51 First, injury-induced tissue GSN can be involved in the activation of monocytes, thus playing a crucial role in inflammation.47 Hence, the increased level of GSN in RA urine samples may reflect inflammatory joint damage. Second, serum levels of ORM1 and ORM2, known as acute-phase inflammatory proteins, were shown to be elevated in RA patients and correlated with RA activity.48,49 Their serum levels were also higher in RA patients than in patients with SLE, mixed connective tissue disease, and Behçet’s disease.49 Such data suggest that the increased levels of ORM1 and ORM2 in both serum or urine could characterize the RA condition and thus serve as useful biomarkers for monitoring RA activity; however, their validity should be tested in large-scale studies. Finally, CD14 is a well-known receptor for lipopolysaccharide, and its serum level well reflects the inflammatory activity of RA.40,41,50,51

Complementary Value of Urinary sCD14 for Disease Activity of RA to Current Serum Measures

The association of urinary sCD14 with the disease activity of RA (Figure 4D) suggests that urinary sCD14 can serve as a complementary measure to a current measure, serum CRP, one of the most useful parameters used currently to predict disease activity of RA.45,46 To examine this, we divided RA patients into two groups with normal (serum CRP < 0.3 mg/dL) and high CRP levels and investigated whether urinary sCD14 reflects disease activity even for RA patients with normal CRP levels. First, 40% of RA patients with normal CRP still showed elevated urinary sCD14 levels more than 2 SD of mean value of OA patients (Supporting Information Table S5). Second, in RA patients with normal CRP, urinary sCD14 had a high positive correlation with conventional measures of RA activity, including ESR and DAS28 score (Figure 5A and B). In RA patients with elevated CRP, it also correlated with ESR (γ = 0.522, P < 0.001) and DAS28 (γ = 0.439, P < 0.001). Third, we further divided RA patients with normal CRP into two subgroups with high (>2 SD of urinary sCD14 level in OA patients) and low urinary sCD14 levels. The high urinary sCD14 subgroup with normal CRP had higher levels of DAS28 score and ESR compared to the low sCD14 subgroup with normal CRP (Supporting Information Table S6). These data suggest that urinary sCD14 reflects disease activity even in the subgroup of active RA patients with normal CRP. Based on this observation, we further examined whether the complementary nature of urinary sCD14 can provide additional predictive value for high RA activity (DAS28 > 5.1) when urinary sCD14 is combined with CRP. To this end, for RA patients with high disease activity, we performed an ROC curve analysis for CRP, urinary sCD14, and a combined measure of CRP and urinary sCD14, as well as other measures related to inflammatory activity (e.g., hemoglobin, platelet, and albumin). 5213

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sCD14 (Supporting Information Figure S4C). Furthermore, in the analysis stratified by ESR and CRP (Supporting Information Figure S4D), the serum sCD14 level increased the probability of an elevated urinary sCD14 level in the subgroup of RA patients with a high ESR (OR = 2.219 [1.122− 4.039], P = 0.021). This was also observed in the subgroup with a high CRP (OR = 2.092 [1.019−4.292], P = 0.044). Moreover, the predicted probability of elevated urinary sCD14 increased with rising serum sCD14 levels in patients with high ESR or CRP, compared to those with low ESR or CRP. Together, these observations suggest that the increase in urinary sCD14 level may result from an overflow of the circulatory pool under systemic inflammatory conditions. Urinary sCD14 showed an overlap in two distributions of its abundances measured from RA and OA sample groups. It would be difficult to assess how much overlap between the distributions of urinary sCD14 in RA and OA samples is allowed to achieve a sufficient diagnostic accuracy. An important issue is whether urinary sCD14 reflects disease activity in the nonoverlapping RA samples (e.g., >3 SD or 5 SD of urinary sCD14 levels in OA patients). To address this issue, we divided RA patients into two groups, high (>5 SD of urinary sCD14 levels in OA patients) and low sCD14 levels. The high sCD14 group had higher ESR and CRP levels, DAS28 scores, and lower albumin levels than the low sCD14 group, suggesting that urinary sCD14 reflects disease activity (Supporting Information Table S8A). However, there was no significance difference in disease severity-related measures in the sera, such as RF and ACPA, between the two groups. A similar association pattern was observed for the groups with high and low urinary sCD14 levels defined by 3SD of urinary sCD14 levels in OA patients (Supporting Information Table S8B). Thus, despite the overlap between the distributions of urinary sCD14 levels in RA and OA samples, urinary sCD14 provides meaningful predictive value for the disease activity of RA. Many biomarkers clinically used or proposed by global profiling studies have moderate correlations with disease activity measures.54 Likewise, urinary sCD14 showed moderate correlations with the measures (ESR and DAS28) for disease activity of RA (Figure 5A and B). The moderate correlations are ascribed partially to the overlap in urinary sCD14 levels between RA and OA patients mentioned above. Given the moderate correlation, the relationship between urinary sCD14 and disease activity of RA should be interpreted “probabilistically” on a population the basis of RA patients. In individual RA patients, however, the correlation between urinary sCD14 and disease activity may not be able to be considered “necessarily deterministic”. To resolve this issue, a longitudinal study can be carried out to provide better understanding of the correlation between urinary sCD14 and disease activity. More importantly, detailed functional studies can be performed to provide the molecular mechanism underlying the elevated urinary sCD14 under RA conditions. On the basis of this mechanism, how urinary sCD14 can reflect RA pathogenesis and how it can differ from other measures in reflecting RA pathogenesis at the molecular level can be examined at the molecular level. In summary, through integrated transcriptomics and proteomics analysis, novel biomarker candidates for RA activity were successfully identified in the urine of RA patients: GSN, ORM1, ORM2, and sCD14. In particular, we demonstrated that urinary sCD14 had a high predictive value for disease activity of RA when combined with a conventional serum marker, CRP. Thus, our approach provided a new dimension

We demonstrated the following experimental evidence supporting the validity of sCD14 as a useful urinary biomarker to predict the disease activity of RA: Urinary sCD14 is elevated in RA patients (Figure 4B), represents comorbidity in RA including DM and hypertension (Figure 4C), correlates positively with clinical parameters associated with inflammatory activity of RA (Figure 4D), and has comparable predictive values for RA activity to serum CRP (Figure 5C). Moreover, in the prediction of disease activity, urinary sCD14 has additional benefits to current serum measures of disease activity (e.g., CRP) (Figure 5). In particular, for RA patients with normal CRP, urinary sCD14 showed a prediction power of RA comparable to that for those with high CRP (Supporting Information Table S5). Also, in the RF-negative subgroup, urinary sCD14 more synergistically increases the probability of high activity with serum CRP than in the RF-positive subgroup (Figure 5E and F). This complementary nature of urinary sCD14 prompted us to develop a ratio of serum CRP to urinary sCD14 as a combined measure of disease activity, which provides higher sensitivity, specificity, and predictive values than serum CPR or sCD14 alone (Figure 5D). RA patients have a high prevalence of subclinical nephropathy, exhibiting microalbuminuria and tubular dysfunction,52,53 which can affect the excretion of urinary proteins including sCD14. Thus, we examined renal function and the presence of proteinuria in urine samples from the third cohort. Proteinuria levels after adjustment for urine creatinine (Supporting Information Figure S3A) were significantly higher in RA patients than in OA patients and SLE patients with no renal involvement. In contrast, the GFR, estimated using the renal disease equation, was not different among RA, OA, and SLE patients (Supporting Information Table S1C). Of note, there was no significant difference in proteinuria levels between RA patients with and without certain medical treatments (Supporting Information Table S4). Furthermore, despite the high level of proteinuria in RA patients, the proteinuria level showed no significant relationship with disease activity of RA (Supporting Information Table S7). This indicates that proteinuria itself does not correlate with the disease activity of RA, unlike urinary sCD14. Interestingly, urinary sCD14 levels showed a positive correlation with the degree of proteinuria (Supporting Information Figure S3B), whereas serum sCD14 levels did not (data not shown). Moreover, RA patients with decreased renal function (GFR < 90 mL/min/ 1.73 m2) had a higher probability of an elevated urinary sCD14 level than those with normal renal function (Supporting Information Figure S3C): odd ratio (OR) = 1.379 [1.059− 1.944] per one SD increase in proteinuria for cases with GFR < 90 mL/min/1.73 m2, P = 0.031; OR = 1.417 [1.083−1.853] per one SD increase in proteinuria for cases with serum creatinine ≥ 0.7 mg/dL, P = 0.010. These data suggest that subclinical renal damage (proteinuria < 150 mg/day) is frequently observed in RA patients and that urinary sCD14 may be associated with subclinical nephropathy in RA patients. The protein level in urine often reflects the amount of proteins in the serum. Thus, we examined whether urinary sCD14 levels correlated with serum sCD14 levels. We found that serum sCD14 concentrations were higher in RA patients (n = 274) than in OA patients (n = 120) (P < 0.001) (Supporting Information Figure S4A). Also, urinary sCD14 levels correlated positively with serum sCD14 levels (Supporting Information Figure S4B). Consistently, serum sCD14 showed similar correlations with clinical parameters to urinary 5214

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Journal of Proteome Research (urinary sCD14) beyond conventional serum parameters for a complementary assessment of disease activity of RA.

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ABBREVIATIONS

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REFERENCES

A/G, albumin/globulin; ACPA, anticyclic citrullinated peptide antibodies; CI, confidence interval; DAS28, disease activity score 28-joint assessment; DEGs, differentially expressed genes; DEPs, differentially expressed proteins; DM, diabetes mellitus; ELISA, enzyme-linked immunosorbent assay; ESR, erythrocyte sedimentation rate; FDR, false discovery rate; GOBP, gene ontology biological process; GFR, glomerular filtration rate; NPV, negative predictive value; NSAIDs, nonsteroidal antiinflammatory drugs; OA, osteoarthritis; PBMCs, peripheral blood mononuclear cells; PPV, positive predictive value; RA, rheumatoid arthritis; ROC, receiver operating characteristic; sCD14, soluble CD14; SLE, systemic lupus erythematosus; SvdH score, Sharp van der Heijde score

ASSOCIATED CONTENT

S Supporting Information *

TableS1. Baseline characteristics of three sets of cohorts. Table S2. The 134 urinary DEPs between RA and OA patients. The list includes protein accession, gene symbol, Entrez Gene ID, protein description, false discovery rate (FDR), log2-foldchange, and human serum detectability for the 134 DEPs. Table S3. Concentrations of five urine biomarker candidates determined by ELISA assays. Table S4. Comparison of urinary sCD14 levels and proteinuria levels according to medication usage. Table S5. Urinary sCD14 levels in RA patients with normal CRP versus in those with high CRP. Table S6. Association of clinical variables with urinary sCD14 levels in RA patients with normal CRP levels. Table S7. No association between proteinuria levels and clinical variables in RA patients. Table S8. Association of clinical variables with high levels of urinary sCD14 in RA patients. Figure S1. SDS-PAGE fractionation of albumin-depleted urinary proteins. FigureS2. Reproducibility in proteomics analysis of RA and OA urine samples. FigureS3. Increased proteinuria levels in RA patients. Figure S4. Serum sCD14 and its association with disease activity of RA. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Authors

*W.-U. Kim. E-mail: [email protected]. Tel.: 82-2-22587530. Fax: 82-2-592-1793. Address: Division of Rheumatology, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 137-701, Korea. *D. Hwang. E-mail: [email protected]. Tel.: 82-53-789-1840. Fax: 82-53-789-1819. Address: Department of New Biology and Center for Systems Biology of Plant Senescence and Life History, Institute for Basic Science, DGIST, Daegu, 711-873, Korea. *E. C. Yi. E-mail: [email protected]. Tel.: 82-2-740-8926. Fax: 822-740-8926. Address: Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University College of Medicine, 28 Yeongeon-Dong, Jongno-Gu, Seoul, 110-799, Korea. Author Contributions ○

The first three authors contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by grants from the Korea Healthcare Technology R&D Project, the Ministry for Health, Welfare and Family Affairs (No. HI09C1555), the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Proteogenomic Research Program, NRF-M1AXA002-2011-0028392), the Institute for Basic Science (CA1308), POSCO research fund (Project NO. 2013Y008), and Research Resettlement Fund for the new faculty of Seoul National University. 5215

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NOTE ADDED AFTER ASAP PUBLICATION This paper was inadvertently published before the corrections were applied. The final corrected version was re-posted on September 19, 2014.

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