AU2015100100A4 - N-Glycans on IgG As Biomarkers for Autoimmune Diseases Explored Via Comprehensive Glycomic Approach - Google Patents

Abstract

Described herein as a method of using microfluidic porous graphitized carbon (PGC) chip-LC-MS-based comprehensive glycomic approach to explore N-glycan biomarkers of several kinds of autoimmune diseases. At least two sets of biomarkers may be identified from serum IgG of patients with the autoimmune diseases. The method includes the steps of isolating IgG from serum samples of patients by Protein A, releasing N-glycans from isolated IgG, separating N-glycans from non-glycan compounds by the chip-LC and analyzing the separated N-glycans by mass spectrometry, screening and identifying acidic N-glycans by comparing deconvoluted experimental masses with theoretical N-glycan masses. PCA PLS-DA OPLS-DA 3-. 6 -5 -6 .4 H 222 111 1111 .- rc <S OL0 0.- P 0 1 Ip<00 . 6- 1.-02 0.0- Cont ol 0. - Co trolRA 00-C o n tro l R 0-- p<0.5 p <0.0 05 pp~ < 0.01 0 30.3- .030 o0.2 = a-a ConrlCntolI oto RA 30333212a 331b 0. p <0.0150 <O 213 aa11b33 0.5-00.0 p.0c0.05 R p<0~t . 0 RA ________R a K .. Figure 2

Description

N-Glycans on IgG As Biomarkers for Autoimmune Diseases Explored Via Comprehensive Glycomic Approach Background of Invention N-Glycans covalently attached on immunoglobulin G (IgG) are often functional determinants of biological events of IgG 1

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2 . IgG recognizes and clears pathogens and toxins through coupling specificity of variable region to Fc-mediated cellular function that is regulated by modulating the composition of the Fc-linked glycans 1

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3

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4 . Close association between variations in the glycosylation of IgG and changes in the immune status of humans has long been appreciated, which facilitated glycoforms of IgG as biomarkers for the diagnosis of various immunological diseases and prediction of immune response5-6. Glycans are encoded in a complex dynamic network of hundreds of genes that participate in the biosynthetic pathway of protein glycosylation, resulting in the generation of extremely complex and variable glycosylation profile for even single glycoprotein. Such immense heterogeneity of glycan structures results in very low concentrations of individual glycoforms, which, however, are often just biologically important. Of note, acidic N-glycans with anionic residues such as sialic acid could further experience ionization bias when comparing to neutral glycans, resulting in lower-than-desired signal intensities 78 . However, these acidic glycans are of growing importance for their biological function. For instance, N-glycans with sialic acid exhibited noticeable anti-inflammatory activity via unique mechanism" 10 . Therefore, comprehensive glycomic approach that embracing the low-abundance, difficult-to-detect (acidic glycans) and biologically important species is desired for the exploration of glycan-based biomarkers. It is therefore a need to utilize a microfluidic porous graphitized carbon (PGC) chip-LC-MS-based comprehensive glycomic approach to explore N-glycan biomarkers of several kinds of autoimmune diseases. This chip-LC-MS-based approach has obvious superiorities in the detection sensitivity and isomer separation. By employing mobile phase favorable to acidic glycans, this approach is capable of detecting considerable number of acidic N-glycans on IgG that are usually exist in low abundance. By using this comprehensive glycomic approach, potential glycomic biomarkers for both 1 autoimmune diseases such as rheumatoid arthritis (RA) and systemic lupus erythematous (SLE) can be identified. Summary of Invention Because N-glycans on IgG are determinants of biological functions of IgG and therefore are potential signatures for various immunological diseases, a comprehensive glycomic approach based on chip-LC-MS is provided with enhanced detection of the acidic and low abundance glycans in the present invention. This approach is then applied to the glycomic analysis of serum IgG of RA and SLE patients, resulting in the successful identification of glycomic biomarkers for these diseases. Embodiments of the Invention The present invention relates to the comprehensive profiling of N-glycans on IgG by using PGC-chip based glycomic approach. The invention also covers the application of this glycomic approach in the identification of biomarkers for RA and SLE. In one embodiment of the present invention, a comprehensive glycomic approach is provided for the profiling of glycosylation of IgG. In one embodiment of the present invention, 12 glycoforms are found to be differentially expressed between RA and healthy subjects and thus are identified as potential biomarkers for RA. In one embodiment of the present invention, 20 glycoforms are found to be differentially expressed between SLE and healthy subjects and thus are assigned as potential biomarkers for SLE. DESCRIPTION OF THE FIGURES Figure 1. Illustrates a multivariate analysis of glycomic data of RA Figure 2. Illustrates the relative abundance of 12 glycomic markers in RA and control groups Figure 3. Illustrates a multivariate analysis of glycomic data of SLE Figure 4. Illustrates the relative abundance of 20 glycomic makers with significant difference between SLE and control groups (* p < 0.5, ** p < 0.01, *** p < 0.001) 2 EXAMPLES The following preparations and examples are given to enable those skilled in the art to more clearly understand and to practice the present invention. They should not be considered as limiting the scope of the invention, but merely as being illustrative and representative thereof. Example 1 This example describes the methodologies for the profiling of N-glycans of serum IgG by using PGC-Chip coupled with TOF-MS. Reagents rProtein A SepharoseTM 4 Fast Flow (90 [tm) and Protein G SepharoseTM 4 Fast Flow (90 [tm) were obtained from GE Healthcare (Uppsala, Sweden). PNGase F (500,000 units/ml) was a product from New England Biolabs Inc. (Beverly, MA, USA). Protein assay dye reagent concentrate was purchased from Bio-Rad (Hercules, CA, USA). Multiscreen solvinert Filter plates (96 wells, 0.45 ptm, hydrophilic PTFE) and Amicon Ultra-0.5 1OOK centrifuge filter devices were purchased from Millipore (Co Cork, Ireland). V-bottom 96-well collection plates and Sep-Pak Cis cartridges were purchased from GE Healthcare and waters (Milford, MA, USA) respectively. All other chemicals were of analytical reagent grade. LC-MS grade acetonitrile and methanol were purchased from Avantor (Center Valley, PA, USA), while LC-MS grade formic acid, acetic acid and ammonia solution were purchased from Sigma-Aldrich (St. Louis, MO, USA). Distilled water was purified by Milli-Q system (Millipore Ltd, Watford, UK). Isolation of IgG from serum sample by using Protein A. rProtein A SepharoseTM 4 Fast Flow beads were washed twice with 5 volumes of binding buffer (20 mM sodium phosphate, pH 7.0). 50 ptL beads per well were applied to a 96-well filter plate. The volume was brought to 250 jaL with binding buffer, and 10 jaL serum was applied per well. The plate was sealed and incubated on a shaker at room temperature for 15 min. The filtrate was collected in a V-bottom collection plate by centrifugation (1000 rpm, 5 min). The retained beads were washed twice with 250 jaL binding buffer. IgGs (IgGI, IgG2 and IgG4) were then eluted twice with 200 jaL elution buffer (0.1 M glycine buffer, pH 2.7) into a new V-bottom collection plate. 30 jaL 3 neutralizing buffer (1 M Tris-HCl, pH 9.0) was subsequently added for neutralization. The IgG samples were transferred to lOOK centrifuge filter units for exchanging buffer to distilled water and concentrated to a final volume of approximately 30 tL. The amount of captured IgGs in each sample was quantitated by Bio-Rad protein assay. Release of N-glycans from IgG. 50 tg IgGs of each sample was taken out and diluted with 100 mM\4 ammonium bicarbonate buffer (pH 7.4) to give a final concentration of 1 pg/pL, 0.5 pl PNGase F was added, followed by a 16-hour incubation at 370C. The cleaved N-glycan was enriched with a Cis cartridge. The Cis cartridge was firstly washed with methanol and then conditioned with distilled water. The cleaved N-glycan sample was directly loaded onto the cartridge and washed with 0.5 mL distilled water. The non-bound and aqueous fraction were combined and dried by speed vacuum. The dried samples were reconstituted in 100 tL distilled water, and stored at -80'C before analysis. Chromatographic separations and MS analysis of N-glycans on serum IgG Samples were analyzed using a microfluidic chip-LC coupled with the Agilent 6550 iFunnel Accurate Mass Quadrupole Time-of-Flight Mass Spectrometer System (Agilent Technologies, Santa Clara, CA, USA) equipped with an auto-sampler (maintained at 5'C), capillary pumps, nano pumps, and a chip/MS interface. The microfluidic chip consisted of a 40-nL enrichment column and a 43xO.075 mm i.d. analytical column, both packed with 5 tm graphitized carbon as the stationary phase, with integrated nano-ESI spray tips. For each sample, 1.0 pL of sample solution was loaded onto the enrichment column and washed with a solution of 0.1% formic acid (v/v) in water. A rapid N-glycan elution gradient was delivered at 0.5 pL/min using solutions of (A) 65 mM formic acid buffered to pH 3 in water and (B) 100% acetonitrile, at the following proportions and time points: 5-60%, 0-12 min; Remaining non-glycan compounds were flushed out with 80% B at 0.5 pL/min for 3 min, while the enrichment column was re-equilibrated with 0.1% formic acid at 3 pL/min for 10 min. The drying gas temperature was set at 225'C with a flow rate of 11 L/min (filtered nitrogen gas). MS spectra were acquired over a mass range of m/z 500-3000 with an acquisition time of 1.0 s per spectrum in positive ionization mode. The instrument was operated using the target MS/MS mode, with the m/z range from m z 50 to 3000 with an acquisition time of 1.5 s per spectrum. 4 Mass correction was enabled using a reference mass of m/z 922.0098 as the internal standard (G1969-85001; Agilent Technologies). The collision energy was set at 5-20 V. The full-width half maximum of the quadrupole mass used during MS/MS precursor isolation was set to medium (~4 m/z). Screening and identification of N-glycans based on accurate mass data LC/MS raw data were processed using the Molecular Feature Extractor (MFE) algorithm (Version B.06.00; Agilent Technologies). The key parameters setting in the MFE include the N-glycan model in the isotope model item, three as the maximum charge states, and 20 ppm as the accurate mass criteria. MS peaks were filtered with a signal-to-noise ratio of 5.0 and parsed into individual ion species. Using the expected isotopic distribution, charge state information and retention time, all ion species associated with a single compound (e.g., the doubly protonated ion, the triply protonated ion, and all associated isotopologues) were totaled, and the neutral monoisotopic mass of the compound was calculated. Using this information, a list of all peaks in the sample was generated with abundances represented by chromatographic peak areas. Computerized algorithms were used to identify N-glycan compositions by accurate mass. By combining these empirical findings with previous research into the N-glycans of mammals, a virtual personal compound database and library (PCDL) was established, which contained all biologically plausible serum N-glycan compositions. Deconvoluted experimental masses were compared against theoretical N-glycan masses using a mass error tolerance of 5 ppm. The scoring of the generated formulas was based on three factors: first, the measured mass (or m z) was compared with the value predicted from the proposed formula; second, the abundance pattern of the measured isotope cluster was compared with values predicted from the proposed formula; third, the m/z spacing between the lowest m/z ion and the A+1 and A+2 ions were compared with the values predicted from the proposed formula. These individual factors were computed as match probabilities. Combining the individual match probabilities into an overall score was done as a weighted average rather than as a product. Results: By using the described approach, totally 171 N-glycans derived from 46 distinct compositions were 5 identified from serum IgG, including 50 neutral glycans derived from 11 compositions and 121 acidic glycans derived from 35 compositions. Among these N-glycans, 21 acidic N-glycans derived from 11 compositions were characterized on IgG for the first time (Table 1). Table 1. N-glycans on serum IgG identified by using Chip-LC-TOF MS Code Name Formula Ion ]2+ Mass Theoretical Diff Isomers [M+2Hf~Ms mass (ppm) 1100 Hex3HexNAc3dHexl C48181N3035 630.7395 1259.464 1259.465 -1.03218 4 1110 Hex4HexNAc3dHexl C54191N3040 1422.5229 1421.516 1421.518 -1.47729 5 2000 Hex3HexNAc4 C50184N4036 659.2478 1316.485 1316.487 -1.06344 6 2010 Hex4HexNAc4 C56194N4041 740.2775 1478.54 1478.539 0.405806 5 2020 Hex5HexNAc4 C62H104N4046 821.3037 1640.592 1640.592 -0.24381 6 2100 Hex3HexNAc4dHexl C56194N4040 732.279 1462.541 1462.544 -2.39309 4 2110 Hex4HexNAc4dHexl C62H104N4045 813.3063 1624.592 1624.597 -3.01613 5 2120 Hex5HexNAc4dHexl C681114N4050 894.3323 1786.648 1786.65 -1.28733 3 3100 Hex3HexNAc5dHexl C641107N5045 833.8189 1665.622 1665.624 -1.26079 3 3110 Hex4HexNAc5dHexl C701117N5050 914.8448 1827.674 1827.677 -1.42257 5 3120 Hex5HexNAc5dHexl C76H127N5055 995.8731 1989.728 1989.73 -0.6031 4 1011 Hex4HexNAc3NeuAcl C59198N4044 784.2845 1566.5519 1566.5554 -2.23 4 1031 Hex6HexNAc3NeuAcl C711118N4054 946.3377 1890.6603 1890.6610 -0.3702 3 1111 Hex4HexNAc3dHexlNeuAcl C651108N4048 857.3137 1712.6130 1712.6133 -0.18 4 1121 Hex5HexNAc3dHexlNeuAcl C711118N4053 938.3402 1874.6649 1874.6661 -0.6401 4 1131 Hex6HexNAc3dHexlNeuAcl C771128N4058 1019.3645 2036.7162 2036.7189 -1.33 4 2011 Hex4HexNAc4NeuAcl C671111N5049 885.8214 1769.6342 1769.6348 -0.34 5 2021 Hex5HexNAc4NeuAcl C731121N5054 966.8516 1931.6880 1931.6876 0.21 5 2022 Hex5HexNAc4NeuAc2 C84H138N6062 1112.3990 2222.7832 2222.7830 0.09 5 2031* Hex6HexNAc4NeuAcl C791131N5059 1047.8773 2093.7379 2093.7404 -1.19 4 2111 Hex4HexNAc4dHexlNeuAcl C731121N5053 958.8540 1915.6926 1915.6927 -0.05 5 2121 Hex5HexNAc4dHexlNeuAcl C791131N5058 1039.8812 2077.7472 2077.7455 0.82 5 2122 Hex5HexNAc4dHexlNeuAc2 C901148N6066 1185.9299 2368.8415 2368.8409 0.25 5 2131 Hex6HexNAc4dHexlNeuAcl C851141N5063 1120.9066 2239.7936 2239.7983 -2.10 2 2222* Hex5HexNAc4dHex2NeuAc2 C96H158N6070 839.6410 2514.8906 2514.8988 -3.26 2 3011 Hex4HexNAc5NeuAcl C75H124N6054 987.3650 1972.7078 1972.7141 -3.19 2 3012* Hex4HexNAc5NeuAc2 C86H141N7062 1141.4212 2263.8005 2263.8053 -2.12 1 3021 Hex5HexNAc5NeuAcl C81H134N6059 1068.3893 2134.7664 2134.7670 -0.28 5 3022 Hex5HexNAc5NeuAc2 C921151N7067 1214.4407 2425.8624 2425.8624 0.00 4 3031 Hex6HexNAc5NeuAcl C87H144N6064 1149.9166 2296.8152 2296.8198 -2.00 3 3032 Hex6HexNAc5NeuAc2 C981161N7072 863.9800 2587.9104 2587.9152 -1.85 3 3033 Hex6HexNAc5NeuAc3 C1091178N8080 961.0112 2879.0076 2879.0106 -1.04 4 3111 Hex4HexNAc5dHexlNeuAcl C811134N6058 1060.3933 2118.7723 2118.7720 0.14 5 3121 Hex5HexNAc5dHexlNeuAcl C871144N6063 1141.9225 2280.8264 2280.8249 0.66 5 6 3122 Hex5HexNAc5dHexlNeuAc2 C98H161N7071 858.6493 2571.9208 2571.9203 0.19 3 3131 Hex6HexNAc5dHexlNeuAcl C93H154N6068 1222.9463 2442.8721 2442.8777 -2.29 5 3132 Hex6HexNAc5dHexlNeuAc2 C104H171N7076 912.6661 2733.9664 2733.9731 -2.45 5 3133 Hex6HexNAc5dHexlNeuAc3 C115H188N8084 1009.6970 3025.0620 3025.0685 -2.15 5 3141* Hex7HexNAc5dHexlNeuAcl C99H164N6073 869.3157 2604.9218 2604.9305 -3.34 2 3231* Hex6HexNAc5dHex2NeuAcl C99H164N6072 1295.9818 2588.9310 2588.9356 -1.78 1 4042* Hex7HexNAc6NeuAc2 C112H184N8082 985.6866 2953.0415 2953.0474 -2.00 2 4043* Hex7HexNAc6NeuAc3 C123H201N9090 1082.7228 3244.1326 3244.1428 -3.14 1 4132* Hex6HexNAc6dHexlNeuAc2 C112H184N8081 980.3577 2937.0403 2937.0525 -4.15 1 4133* Hex6HexNAc6dHexlNeuAc3 C123H201N9089 1077.3869 3228.1341 3228.1479 -4.27 3 4142* Hex7HexNAc6dHexlNeuAc2 Cl18H194N8086 1034.3764 3099.1015 3099.1053 -1.23 2 4143* Hex7HexNAc6dHexlNeuAc3 C129H211N9094 1131.4075 3390.1934 3390.2007 -2.15 2 * N-glycans that were identified on IgG for the first time Conclusion: The described method is a comprehensive approach for the profiling of N-glycans on IgG. It holds great promise for the discovery of novel glycomic biomarkers for various diseases. Example 2 This example describes the identification of potential biomarkers for RA. Collection of clinical samples Serum samples of 13 RA patients and 14 healthy subjects (control group, gender and age matched with that of RA group) were collected from the First People's Hospital of Jiujiang City and were stored at -80 'C prior to sample preparation. All samples were prepared in duplicate and subjected to LC-MS analyses in duplicate. Multivariate analysis The data were analyzed using the software of Agilent MassHunter Quantitative Analysis (version B.05.00, Agilent Technologies). The intensity of each peak obtained was normalized as the percentage peak intensity relative to the total peak intensity in that sample. Multivariate statistical data analyses, including a principal components analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), and orthogonal partial least squares discriminate analysis (OPLS-DA), were performed using the SIMCA 13.0 software (Umetrics, Umea, Sweden). The scores plot of the PCA displays the general clustering, trends, or outliers in the observations (or samples), whereas PLS-DA 7 and OPLS-DA are supervised methods capable of removing information from an input data set X that is unrelated to the response set Y to improve the interpretation of the variations responsible for the separation. The major differences in glycosylation between the responders and nonresponders were determined based on loading plots and the variable importance parameters (VIPs) for each pattern recognition model. The loading plots display the correlation between the X variables, in the first dimension, or the residuals of the X variables in subsequent dimensions. The VIP indicates the effect on class membership. A VIP value larger than 1 is more successfully explains Y than smaller VIP values. In general, the glycosylation that contributed to the variation in the present study was identified by ranking the VIP values, and further validated with a univariate analysis (Student's t test). Statistical analysis Quantitative characteristics are presented as means ± SD. Data that were normally distributed were compared using analysis of variance (ANOVA). All hypothesis testing was two-sided and P < 0.05 was defined as significant. Results To observe the differentially expressed IgG glycosylation between RA and control group, the glycomic data were first using principal components analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA). When using PLS-DA model, a clear trend separating the RA and control group was observed, implying that glycosylation was differentially expressed in RA and control group (Figure 1). A further orthogonal partial least squares discriminate analysis (OPLS-DA) was then performed, which revealed an obvious separation between RA and control subj ects. The major differences in glycosylation between RA and healthy subjects were determined from the loading plot and the VIP values for the OPLS-DA model, enabling the identification of 12 potential glycomic markers for RA (Table 2). These 12 acidic glycomic markers showed significant difference (p < 0.05) between RA and control group (Figure 2). As can be seen from Table 2, the differentiated glycoforms were predominantly associated with mono-sialylated glycans. 8 Table 2. Potential glycomic biomarkers for RA Glycoform VIP value Relative response (mean J SD) Change Control RA 2222 2.24 0.13 ± 0.07 0.02 ± 0.04 1111_a 1.84 0.15 ±0.04 0.22 0.07 1111_b 1.79 1.17 ± 0.26 1.65 ± 0.39 2131_b 1.63 0.19 ±0.04 0.14 0.04 3032_a 1.46 0.45 ± 0.16 0.65 ± 0.24 3031_b 1.43 0.10 ±0.05 0.16 0.07 2131_a 1.43 0.28 0.06 0.22 0.05 2121_b 1.43 30.51 ± 5.14 25.38 ± 5.02 3031_a 1.31 0.06 0.03 0.09 0.03 3033_a 1.28 1.03 ± 0.30 1.37 ± 0.48 2121_a 1.28 6.95 ± 2.01 5.44 ± 1.17 3133_a 1.26 1.20 ± 0.25 1.68 ± 0.73 Conclusion: Totally 12 glycoforms were identified as potential biomarkers for RA. These biomarkers can be employed for the diagnosis of RA. Example 3 This example describes the identification of potential biomarkers for SLE. Collection of clinical samples Serum samples of 12 SLE patients and 12 healthy subjects (control group, gender and age matched with RA group) were collected from the First People's Hospital of Jiujiang City and were stored at -80 'C prior to sample preparation. All samples were prepared in duplicate and subjected to LC-MS analyses in duplicate. Multivariate analysis and statistical analysis Both analyses were performed in the same method as that for RA Results The glycomic data for the individual glycans were visualized using principal components analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA). Clear separation between SLE and control group can be observed in both PCA and PLS-DA model, suggested significant 9 difference in the glycosylation pattern of these two groups (Figure 3). The major differentially expressed glycoforms were explored by using the loading plot and the VIP values for the PLS-DA model. As a result, 22 acidic glycans were characterized as potential biomarkers (Table 3). Among these potential biomarkers, 20 N-glycans were further confirmed by univariate analysis (Figure 4). These 20 glycoforms can be regarded as potential biomarkers for SLE. Table 3. Potential glycomic biomarkers for SLE Glycoform VIP value Relative response(mean SD) Change Control SLE 3022-a* 1.63 0.43±0.11 0.06±0.06 3022-b* 1.61 1.09±0.29 0.21±0.13 2022-a* 1.49 8.80±1.82 3.50±1.53 3021-b* 1.45 0.12±0.03 0.05±0.03 2022-b* 1.40 17.51±2.23 8.91±3.98 2022-c* 1.33 2.36±0.59 1.14±0.45 2121-b* 1.29 13.56±4.28 27.73±8.52 1131* 1.28 0.26±0.13 0.72±0.29 2121-a* 1.27 5.25±2.02 10.65±3.16 2021-b* 1.18 2.03±1.13 0.79±0.60 1111* 1.16 0.61±0.09 0.84±0.17 3121-b* 1.16 0.36±0.21 3.41±2.64 2111-a* 1.15 0.88±0.31 1.72±0.66 2111-b* 1.15 1.31 0.79 3.31±1.55 3032-b* 1.12 0.36 ±0.14 0.62±0.27 3033-a* 1.10 1.97±0.67 1.12±0.46 1011* 1.09 0.43±0.05 0.30±0.12 2122-a* 1.09 4.46±1.59 2.35±1.22 3033-b* 1.05 2.45±0.61 1.56± 0.63 3131* 1.05 0.23±0.15 0.64±0.53 4132 1.01 0.04±0.05 0.16±0.18 2021-a 1.00 0.64±0.30 0.48±0.26 * confirmed by univariate analysis REFERENCES 1 Ujike, A. et al. Modulation of immunoglobulin (Jg)E-mediated systemic anaphylaxis by low-affinity Fc receptors for IgG. The Journal of experimental medicine 189, 1573-1579 10 (1999). 2 Nimmerjahn, F., Anthony, R. M. & Ravetch, J. V. Agalactosylated IgG antibodies depend on cellular Fc receptors for in vivo activity. Proceedings of the National Academy of Sciences of the United States ofAmerica 104, 8433-8437, doi:10.1073/pnas. 0702936104 (2007). 3 Baudino, L. et al. Differential contribution of three activating IgG Fc receptors (FcgammaRI, FcgammaRIII, and FcgammaRIV) to IgG2a- and IgG2b-induced autoimmune hemolytic anemia in mice. Journal of immunology 180, 1948-1953 (2008). 4 Boruchov, A. M. et al. Activating and inhibitory IgG Fc receptors on human DCs mediate opposing functions. The Journal of clinical investigation 115, 2914-2923, doi: 10.11 72/JC124772 (2005). 5 Tomana, M., Schrohenloher, R. E., Koopman, W. J., Alarcon, G. S. & Paul, W. A. Abnormal glycosylation of serum IgG from patients with chronic inflammatory diseases. Arthritis and rheumatism 31, 333-338 (1988). 6 van de Geijn, F. E. et al. Immunoglobulin G galactosylation and sialylation are associated with pregnancy-induced improvement of rheumatoid arthritis and the postpartum flare: results from a large prospective cohort study. Arthritis research & therapy 11, R193, doi: 10.11 86/ar2892 (2009). 7 Deguchi, K. et al. Two-dimensional hydrophilic interaction chromatography coupling anion-exchange and hydrophilic interaction columns for separation of 2-pyridylamino derivatives of neutral and sialylated N-glycans. Journal of chromatography. A 1189, 169-174, doi:10.1016/j.chroma.2007.09.028 (2008). 8 Hanneman, A. J., Strand, J. & Huang, C. T. Profiling and characterization of sialylated N-glycans by 2D-HPLC (HIAX/PGC) with online orbitrap MS/MS and offline MSn. Journal ofpharmaceutical sciences 103, 400-408, doi:10.1002/jps.23 792 (2014). 9 Wu, Z. L., Prather, B., Ethen, C. M., Kalyuzhny, A. & Jiang, W. Detection of specific glycosaminoglycans and glycan epitopes by in vitro sulfation using recombinant sulfotransferases. Glycobiology 21, 625-633, doi: 10.1093/glycob/cwq2O4 (2011). 10 Kaneko, Y., Nimmerjahn, F. & Ravetch, J. V. Anti-inflammatory activity of immunoglobulin G resulting from Fc sialylation. Science 313, 670-673, doi:10.1126/science.1129594 (2006). 11

Claims (5)

1. A method of using microfluidic porous graphitized carbon (PGC) chip-LC-MS-based comprehensive glycomic approach to explore N-glycan biomarkers of several kinds of autoimmune diseases such that at least two sets of biomarkers are identified from serum IgG of patients with the autoimmune diseases, said method comprising isolating IgG from serum samples of the patients by Protein A, releasing N-glycans from isolated IgG, separating N-glycans from non-glycan compounds by the chip-LC and analyzing the separated N-glycans by mass spectrometry, screening and identifying acidic N-glycans by comparing deconvoluted experimental masses with theoretical N-glycan masses, wherein total of 171 N-glycans derived from 46 distinct compositions are identified from serum IgG, and among the 171 N-glycans, 21 acidic N-glycans derived from 11 compositions are characterized on IgG for the first time.

2. The method of claim 1, wherein said method further comprises performing multivariate statistical data analyses including one or more a principal components analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), and/or orthogonal partial least squares discriminate analysis (OPLS-DA), and wherein the major differences in glycosylation of serum IgG between healthy subjects and patients are determined based on loading plots and the variable importance parameters (VIPs) in order to obtain a profile of differentially expressed IgG glycosylation pattern between the patients with a specific autoimmune disease and the healthy subjects.

3. The method of claim 1 or 2, wherein the autoimmune disease is rheumatoid arthritis (RA) and twelve acidic N-glycans are identified which are specific in RA patients, and wherein the twelve acidic N-glycans are predominantly associated with mono-sialylated glycans.

4. The method of claim 1 or 2, wherein the autoimmune disease is systemic lupus erythematous (SLE) and twenty acidic N-glycans are identified which are specific in SLE patients, and wherein the twenty acidic N-glycans are predominantly associated with mono-sialylated glycans.

5. The method of claim 1 or 2, wherein the autoimmune diseases comprise rheumatoid arthritis and systemic lupus erythematous. 12