Serum protein profiling of systemic lupus erythematosus and systemic sclerosis using recombinant antibody microarrays

Abstract

Systemic lupus erythematosus (SLE) and systemic sclerosis (SSc) are two severe autoimmune connective tissue diseases. The fundamental knowledge about their etiology is limited and the conditions display complex pathogenesis, multifaceted presentations, and unpredictable courses. Despite significant efforts, the lack of fully validated biomarkers enabling diagnosis, classification, and monitoring of disease activity represents significant unmet clinical needs. In this discovery study, we have for the first time used recombinant antibody microarrays for miniaturized, multiplexed serum protein profiling of SLE and SSc, targeting mainly immunoregulatory proteins. The data showed that several candidate SLE-associated multiplexed serum biomarker signatures were delineated, reflecting disease (diagnosis), disease severity (phenotypic subsets), and disease activity. Selected differentially expressed markers were validated using orthogonal assays and a second, independent patient cohort. Further, biomarker signatures differentiating SLE versus SSc were demonstrated, and the observed differences increased with severity of SLE. In contrast, the data showed that the serum profiles of SSc versus healthy controls were more similar. Hence, we have shown that affinity proteomics could be used to de-convolute crude, nonfractionated serum proteomes, extracting molecular portraits of SLE and SSc, further enhancing our fundamental understanding of these complex autoimmune conditions.

Fig. 1.

Expression profiling of serum proteomes from SSc and SLE using a 135-recombinant-antibody microarray. The samples were classified using a leave-one-out cross validation approach with a SVM, and illustrated as ROC curves. The patients are ordered by decreasing decision value as assigned by the SVM classifier (middle panel). Differentially expressed analytes are shown in heat maps: green, down-regulated; red, up-regulated; and black, equal levels. A, Classification of healthy controls (blue) versus SSc (red). Four differentially expressed analytes, recognized by five antibodies, were identified. B, Classification of healthy controls (blue) versus SLE (red). Forty nonredundant differentially expressed analytes, recognized by 58 antibodies were identified, of which the 25 highest ranked, i.e. significantly differentially expressed (p values), analytes are shown. C, Classification of SSc (blue) versus SLE (red). Forty-two nonredundant differentially expressed analytes, recognized by 62 antibodies were identified, of which the 25 highest ranked analytes (p values) are shown.

Fig. 2.

Expression profiling of serum proteomes from clinical subsets of SSc, dcSSc, and lcSSc, using a 135-recombinant-antibody microarray. A, Classification of dcSSc and lcSSc versus healthy controls based on all 135 antibodies, using a SVM-based leave-one-out cross validation test, expressed in terms of AUC values. AUC values obtained when using only significantly differentially expressed analytes are given within brackets. B, Significantly differentially expressed analytes are shown in a heat map. Seven differentially expressed analytes, recognized by seven antibodies were identified for dcSSc versus controls; five analytes, recognized by five antibodies for lcSSc versus controls; whereas none were observed for dcSSc versus lcSSc. Green, down-regulated; red, up-regulated; and black, equal levels. The color represent the fold change of a particular marker across all samples within each sample cohort, calculated using the average signal intensities.

Fig. 3.

Expression profiling of phenotypic subsets of SLE reflecting increased disease severity, including SLE1 (least symptoms), SLE2, and SLE3 (most severe symptoms), using a 135-recombinant-antibody microarray. The samples were classified using a leave-one-out cross validation approach with a SVM, and illustrated as ROC curves. The patients are ordered by decreasing decision value as assigned by the SVM classifier (middle panel). Differentially expressed analytes are shown in heat maps: green, down-regulated; red, up-regulated; and black, equal levels. The color represent the fold change of a particular marker across all samples within each sample cohort, calculated using the average signal intensities. A, Classification of SLE1 (red) versus controls (blue). B, Classification of SLE2 (red) versus controls (blue). C, Classification of SLE3 (red) versus controls (blue). The top 25 highest ranked, i.e. significantly differentially expressed (p values), analytes are shown. D, Fifteen significantly differentially expressed analytes, recognized by 17 antibodies, were identified for SLE1 versus controls; 29 analytes, recognized by 36 antibodies for SLE2 versus controls; and 44 analytes recognized by 72 antibodies for SLE3 versus controls.

Fig. 4.

Expression profiling of phenotypic subsets of SLE reflecting increased disease severity and SSc, using a 135-recombinant-antibody microarray. The samples were classified using a leave-one-out cross validation approach with a SVM, and illustrated as ROC curves. The patients are ordered by decreasing decision value as assigned by the SVM classifier (middle panel). Differentially expressed analytes are shown in heat maps: green, down-regulated; red, up-regulated; and black, equal levels. The color represent the fold change of a particular marker across all samples within each sample cohort, calculated using the average signal intensities. A, Classification of SLE1 (red) versus SSc (blue). B, Classification of SLE2 (red) versus SSc (blue). C, Classification of SLE3 (red) versus SSc (blue). The top 25 highest ranked, i.e. significantly differentially expressed (p values), analytes are shown. D, Three significantly differentially expressed analytes, recognized by three antibodies, were identified for SLE1 versus SSc; 36 analytes, recognized by 47 antibodies for SLE2 versus SSc; and 49 analytes recognized by 79 antibodies for SLE3 versus SSc.

Fig. 5.

Expression profiling of phenotypic subsets of SLE reflecting increased disease severity, using a 135-recombinant-antibody microarray. A, Classification of SLE1, SLE2 and SLE3 based on all 135 antibodies, using a SVM-based leave-one-out cross validation test, expressed in terms of AUC values. AUC values obtained when using only significantly differentially expressed analytes are given within brackets. B, PCA analysis of SLE1 (green), SLE2 (yellow), and SLE3 (red) based on the top 13 significantly differentially antibodies using Qlucore. C, Significantly differentially expressed analytes are shown in a heat map. Twenty-three differentially expressed analytes, recognized by 28 antibodies, were identified for SLE1 versus SLE2; seven analytes, recognized by eight antibodies for SLE2 versus SLE3; and 32 analytes recognized by 47 antibodies for SLE1 versus SLE3. Green, down-regulated; red, up-regulated; and black, equal levels. The color represent the fold change of a particular marker across all samples within each sample cohort, calculated using the average signal intensities. D, Microarray signal intensities observed for key Th1 (IL-2, IL-12, and IFN-γ) and Th2 (IL-4, and IL-10) cytokines shown as boxplots. The median values are indicated (thick line) and the hinges represent the 25th percentile and the 75th percentile, respectively.

Fig. 6.

Expression profiling of subsets of SLE reflecting disease activity, using a 135 recombinant antibody microarray. A, Classification of SLE, grouped based solely on disease activity (SLEDAI values); low (3 to 6), mid (9 to 19) and high (22 to 34), based on all 135 antibodies, using a SVM-based leave-one-out cross validation test, expressed in terms of AUC values. AUC values obtained when using only significantly differentially expressed analytes are given within brackets. B, Significantly differentially expressed analytes are shown in a heat map. Twelve differentially expressed analytes, recognized by 14 antibodies, were identified for low versus mid; eight analytes, recognized by 12 antibodies for mid versus high; and 10 analytes recognized by 16 antibodies for low versus high. Green, down-regulated; red, up-regulated; and black, equal levels. The color represent the fold change of a particular marker across all samples within each sample cohort, calculated using the average signal intensities. C, Microarray signal intensities observed for complement proteins, including C1q, C3, C4, and C5, shown as boxplots. The median values are indicated (thick line) and the hinges represent the 25th percentile and the 75th percentile, respectively. D, The correlation between array signal intensities for C1q and SLE disease activity, expressed as SLEDAI-2K.

Fig. 7.

Expression profiling of subsets of SLE3 reflecting disease activity, using a 135 recombinant antibody microarray. Classification of SLE3 subsets, grouped based on disease activity (SLEDAI values); SLE3 low (blue) (mean 13, range 10–16), SLE high (red) (mean 24, range 17–32), based on all 135 antibodies, using a SVM-based leave-one-out cross validation test, expressed in terms of AUC values. The patients are ordered by decreasing decision value as assigned by the SVM classifier (middle panel). The 20 signficantly differentially expressed analytes, recognized by 31 antibodies, are shown in a heat map. SSc. Green, down-regulated; red, up-regulated; and black, equal levels.