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. 2023 Aug 28;22(10):3254–3263. doi: 10.1021/acs.jproteome.3c00268

Identification of Anti-SNRPA as a Novel Serological Biomarker for Systemic Sclerosis Diagnosis

Chenxi Liu †,#, Guang Song ‡,§, Songxin Yan †, Yangzhige He ∥, Chaojun Hu ⊥, Yong Hou ⊥, Xiaoting Wen †, Liubing Li †, Fengchun Zhang ⊥, Heng Zhu §,*, Yongzhe Li †,*
PMCID: PMC10563158  PMID: 37639699

Abstract

graphic file with name pr3c00268_0007.jpg

Systemic sclerosis (SSc) is a systemic autoimmune disorder that leads to vasculopathy and tissue fibrosis. A lack of reliable biomarkers has been a challenge for clinical diagnosis of the disease. We employed a protein array-based approach to identify and validate SSc-specific autoantibodies. Phase I involved profiled autoimmunity using human proteome microarray (HuProt arrays) with 90 serum samples: 40 patients with SSc, 30 patients diagnosed with autoimmune diseases, and 20 healthy subjects. In Phase II, we constructed a focused array with candidates identified antigens and used this to profile a much larger cohort comprised of serum samples. Finally, we used a western blot analysis to validate the serum of validated proteins with high signal values. Bioinformatics analysis allowed us to identify 113 candidate autoantigens that were significantly associated with SSc. This two-phase strategy allowed us to identify and validate anti-small nuclear ribonucleoprotein polypeptide A (SNRPA) as a novel SSc-specific serological biomarker. The observed positive rate of anti-SNRPA antibody in patients with SSc was 11.25%, which was significantly higher than that of any disease control group (3.33%) or healthy controls (1%). In conclusion, anti-SNRPA autoantibody serves as a novel biomarker for SSc diagnosis and may be promising for clinical applications.

Keywords: HuProt array, systemic sclerosis, and biomarkers

1. Introduction

Systemic sclerosis (SSc) is a systemic autoimmune disorder leading to vasculopathy, tissue fibrosis, and production of autoantibodies.1,2 The clinical manifestations of SSc are patient-specific, with most patients with SSc eventually developing skin thickening with variable organ involvement, including interstitial lung disease (ILD), pulmonary arterial hypertension (PAH), and renal crisis.3,4 Autoantibodies are important biomarkers for SSc, aiding in diagnosis, classifying patients into more homogeneous groups, and understanding additional clinical complications and responses to treatment. Previous publications suggested that a reliable autoantibodies profile could be considered as key classification criteria for patients with SSc.5 According to the latest standard classification criteria for SSc, released by the American College of Rheumatology (ACR) and European League Against Rheumatism (EULAR) in 2013,2 the SSc diagnostic scoring system was developed, including calculations based on patient autoantibody profiles (i.e., anti-Scl-70, anti-centromere [CENP], and anti-RNA polymerase III). TOP1MT is the primary antigen of anti-Scl-70, CENPA and CENPB are the main antigens of CENP, and POLR3K is the major antigen of anti-RNA polymerase III.

However, the prevalence of the above-mentioned SSc autoantibodies remains low based on clinical observations. For example, several reports revealed the anti-Scl-70 autoantibody, a well-known biomarker for SSc, was positive in only 30.9–32.3% of SSc patients in Asia.6−8 Therefore, identifying new SSc-specific autoantibody biomarkers would be vital to improve the diagnosis. In the past years, numerous high-throughput methods, such as two-dimensional gel electrophoresis,9 phage display technique,10 indirect immunofluorescence,11 and serological analysis of recombinant cDNA expression libraries (SEREX),12 were performed to discover autoimmune disease-associated biomarkers. However, these methods are laborious, time-consuming, and costly because of requiring additional deconvolution steps, such as large-scale sequencing and mass spectrometry. Additionally, human proteome microarray (i.e., HuProt array) has been developed and subjected to unbiased profiling of novel biomarkers for a series of autoimmune diseases and cancers.13−16 Here, the HuProt array was employed to comprehensively screen for novel autoantibodies associated with SSc with a two-phase strategy, which was applied to autoantibody biomarker discovery in other diseases.

2. Materials and Methods

2.1. Assembly of Cohorts

The SSc cohort employed in this study included 440 clinically diagnosed patients with SSc (38 males and 402 females, mean age 48.5 ± 12.8 years), who fulfilled the 2013 ACR/EULAR classification criteria for SSc. The disease control group of 190 patients with connective tissue disease (CTD) (mean age 47.0 ± 15.7 years) was comprised of the following: 44 patients with primary Sjögren’s syndrome (pSS) who fulfilled the American-European consensus group classification criteria,17 42 patients with rheumatoid arthritis (RA) who fulfilled the 1987 ACR revised classification criteria,18 44 patients with systemic lupus erythematosus (SLE) fulfilling the 1997 ACR revised classification criteria,19 and 30 patients with dermatomyositis (DM) and 30 patients with polymyositis (PM) diagnosed according to the Bohan and Peter criteria.20,21 Healthy subjects (n = 120; mean age 46.5 ± 12.4 years) admitted to the Peking Union Medical College Hospital Health Examination Center for physical examination were recruited as negative controls. Finally, 40 chronic disease controls were also included, including those suffering from diabetes, hypertension, and common malignant tumors. All samples were obtained from patients recruited from the Peking Union Medical College Hospital (PUMCH), and sera samples were stored at −80 °C until use. This study was approved by the Ethics Committee of the PUMCH (approval number JS-2145).

2.2. Construction and Quality Control of Protein Microarrays

The current study employed two types of protein microarrays: HuProt (CDI Labs) and SSc-focused arrays (CDI Labs) for Phase I and II studies, respectively. Each HuProt array was comprised of 21,148 individually purified full-length human proteins as previously described.17 SSc-focused arrays were comprised of 113 proteins identified as potential biomarkers in Phase I. All proteins were N-terminally tagged GST fusions and printed in duplicate on slides (OPEpoxySlide). Thus, the quality of these arrays can be verified by the number of detectable proteins and consistency of duplicate protein signals via fluorescence intensity of the GST fusions.

2.3. Serum Profiling Using HuProt Arrays

In Phase I, HuProt arrays were used to profile autoantibodies from a smaller cohort, including 40 patients with SSc, 30 autoimmune disease controls (7 pSS, 6 RA, 7 SLE, 5 DM, and 5 PM), and 20 healthy controls. Each HuProt array was blocked with 3% bovine serum albumin (BSA) in PBST (PBS with 0.1% Tween) for 1.5 h. Then, 180 μL of diluted sera samples (1:1000) were added and incubated for 1 h with gentle shaking. The arrays were then washed in PBST followed by incubation with 180 μL of diluted goat anti-human IgG conjugated to Alexa 647 (1:1000; The Jackson Laboratory, Bar Harbor, ME) for 1 h with rocking in the dark. After washing with PBST followed by deionized water, the slides were dried, scanned with the GenePix 4000B Microarray Scanner (Molecular Devices, Sunnyvale, CA), and analyzed with GenePix Pro 6.0 software (Molecular Devices).

2.4. Fabrication of SSc-Focused Array and Serum Assay

In Phase II, SSc-focused arrays were used to validate 113 candidate autoantigens in a large cohort consisting of 400 patients with SSc, 160 controls with autoimmune disease (36 RA, 37 SLE, 37 pSS, 25 DM, and 25 PM), 40 chronic disease controls, and 100 healthy controls. The characteristics of study participants involved in this study are shown in Table S1 in the Supporting Information. This procedure was similar to that of the HuProt array, with the following exceptions. A single slide was divided into 12 identical subarrays using a 12-hole rubber gasket so that each chip could test 12 samples simultaneously, with 50 μL of diluted serum (1:1000) and detection antibodies incubated in each block.

2.5. Western Blot Analysis

Small nuclear ribonucleoprotein polypeptide A (SNRPA) protein was expressed and purified from yeast as GST fusions by CDI Labs. The easily purified protein was separated by electrophoresis on sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) (EZBiolab) and then electrotransferred onto poly(vinylidene fluoride) membranes. After blocking with 5% BSA in PBST, the membranes were incubated with serum (1:200) for 2 h. Membranes were washed three times with TBST, followed by incubation with HRP-conjugated goat anti-human IgG (EASYBIO; 1:5000) for 1 h. After washing with TBST and deionized water, the immunoreactive bands were detected by the sensitive chemiluminescence substrate (Thermo Fisher Scientific) and visualized using Clinx Chemical Capture (Science Instruments).

2.6. Statistical Analysis

Image processing and data acquisition from both arrays were performed with GenePix Pro 6.0 (Molecular Devices). Candidate autoantigens associated with SSc were picked up following the procedure described by Hu et al.22 In detail, for each protein, the mean median foreground to median background signal-to-noise ratio was calculated as its signal value in data acquisition. The cutoff value of each protein was defined as the peak value + 8 standard deviations (SD) of healthy controls. Autoantibody candidates were determined based on the following criteria: (1) statistical significance shown by χ2 test or Fisher’s exact test for positivity rate (P < 0.05) when comparing SSc patients with both disease controls and healthy controls; (2) statistical significance shown by χ2 test or Fisher’s exact test for positivity rate (P < 0.05) when comparing SSc patients with and without certain complications, such as interstitial lung disease, pulmonary arterial hypertension, renal crisis, and tumors; (3) previously reported in the literature.

For the focused microarrays, the cutoff value of each protein was defined as the mean + 3 SD of healthy controls. The hit rate of HuProt arrays among groups and the clinical characteristics between the autoantibody positive and negative groups were evaluated using a χ2 test. A machine learning method was performed to predict the diagnostic value of antigens screened with SSc-focused microarrays. Anti-CENPB, CENPA, TOP1MT, and POLR3K were detected by SSc-focused microarrays.

Gene ontology (GO) enrichment analysis of 113 candidate autoantigens was implemented using the GOseq R package. GO terms with corrected P value < 0.05 were considered significantly enriched. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database resource that helps to understand the high-level functions and utilities of the biological system. KOBAS 2.0 was utilized to test statistical enrichment in KEGG pathways. Western blot (WB) signal intensities were measured with Image J software and analyzed with Microsoft Excel.

3. Results

3.1. HuProt Arrays Quality Test

To ensure the accuracy and reproducibility of our studies, the quality of the HuProt arrays was evaluated using the anti-GST antibody because all of the recombinant human proteins were expressed and purified based on their N-terminal GST tag and then were printed on each HuProt array duplicately. To examine the amount and consistency of each human protein on the arrays, the quality was evaluated by comparing the consistency of the signal intensity of each protein pair. As shown in Figure S1 (Supporting Information), the correlation coefficient of the signal intensity consistency of two points of the same protein equaled 0.952, with an R2 value of 0.935. These results indicated that the quality of HuProt arrays was suitable for the serum profiling experiments.

3.2. Identification of SSc-Associated Autoantigens Using HuProt Arrays

A two-phase strategy, consisting of the discovery phase (Phase I) and validation phases (Phase II), was performed to identify the candidate SSc-associated autoantigens efficiently, which was applied in our previous publication.14 In Phase I, HuProt arrays containing 21,148 human proteins individually were subjected to global identification of candidate autoantigens that were potentially associated with SSc. After stringent washing and detection with Alexa 647-labeled goat anti-human IgG, the immune-reactivity signals of sera to each protein on the array were captured and subjected to identify positive hits, using a stringent cutoff value of 8 SD over the controls. The number of positive hits (i.e., autoantigens) among all the serum samples varied dramatically (Figure S2, Supporting Information). Obviously, the positive hits in SSc seem much higher than that from disease and healthy controls. A boxplot analysis reveals that the median of positives identified in SSc sera was 139 (Q1 = 118; Q3 = 151), significantly higher than those obtained within healthy (median = 41; Q1 = 29; Q3 = 61) and the disease control group (median = 58; Q1 = 47; Q3 = 68) (P < 0.0001) (Figure 1).

Figure 1.

Figure 1

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Boxplot analysis of SSc-positive proteins. The number of positive proteins identified in each serum profiling reaction varies significantly between samples. The median number of positive autoantigens identified in the SSc group (139) is significantly higher than that in the healthy group (41) and the disease control group (58) (P < 0.0001). SSc = systemic sclerosis; **** P < 0.0001.

Statistical analysis suggested that a total of 113 autoantigens were significantly associated with SSc (Table S2, Supporting Information). To our knowledge, the vast majority of these proteins have not yet been reported as autoantigens in autoimmune diseases by far. Representative reactive images of typical candidate autoantigens, such as SNRPA and platelet-derived growth factor receptor β (PDGFRB), are shown in Figure 2A. The cluster map of representative autoantigens and row clustering trend are presented in Figure 2B,C. We noted that some of the 113 autoantigens exhibited high sensitivity and specificity in Phase I. For example, 27.5% of the SSc serum samples exhibited significant serum reactivity to SNRPA, while both the disease and the healthy controls failed to recognize this protein, suggesting that it might be a promising marker for SSc diagnosis.

Figure 2.

Figure 2

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(A) Anti-SNRPA antibody was observed in patients with SSc, but not in the disease control or healthy control patients. Red boxes indicate SNRPA autoantigens. (B) Cluster map of 38 representative autoantigens. (C) Row clustering trend graph of 38 representative autoantigens. SSc = systemic sclerosis, SNRPA = small nuclear ribonucleoprotein polypeptide A, PDGFRB = platelet-derived growth factor receptor β.

GO and KEGG pathway analyses were performed to discover the enrichment pattern of the 113 candidate autoantigens (Figure 3). We found that the major significantly enriched biological processes for the 113 candidate autoantigens were positive regulation of type I interferon production, mRNA splicing, via spliceosome, RNA splicing, transcription from RNA polymerase III promoter, DNA recombination, DNA duplex unwinding, viral process, cellular hyperosmotic salinity response, xenophagy, and so on. The major significantly enriched cellular components were nucleoplasm, U12-type spliceosomal complex, cytosol, cytoplasm, nucleus, nucleolus, DNA-directed RNA polymerase III complex, U1 small nuclear ribonuclear proteins (snRNP), membrane, spliceosomal complex, and so on. The significantly enriched molecular functions were poly(A) RNA binding, RNA binding, nucleotide binding, RNA polymerase III activity, protein binding, ATP-dependent DNA helicase activity, glutamate decarboxylase activity, platelet-activating factor receptor activity, PDGFRB receptor activity, and so on. The results of the KEGG pathway analysis demonstrated that the 113 candidate autoantigens were significantly enriched in RNA polymerase and ribosome biogenesis in eukaryotes.

Figure 3.

Figure 3

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(A) Gene ontology analysis results for the 113 candidate autoantigens. (B) Kyoto Encyclopedia of Genes and Genomes analysis results for the 113 candidate autoantigens.

3.3. Validation of SSc-Associated Autoantigens Using SSc-Focused Arrays

To ensure reproducibility and avoid potential overfitting problems, we fabricated SSc-focused arrays with 2 × 6 subarray format using the re-prepared 113 candidate autoantigens in the Phase II validation and subjected them to the immune reactivity with a much larger cohort, comprising 700 serum samples, including 400 patients with SSc, 100 healthy subjects, 40 patients with chronic diseases, and 160 patients with various autoimmune diseases. Same as our previous profiling assay based on focused array for other diseases,13,14,23 all of the serum samples were separately profiled in each block on the focused arrays.

After data acquisition and biostatistics analysis as before,13,14,23 the SNRPA protein has the best performance as SSc-specific autoantigen and was therefore selected for further validation. Anti-SNRPA could distinguish SSc samples from both healthy and disease controls with a 11.25% sensitivity in SSc and a 96.67% specificity, for which 290 were negative among the 300 controls, respectively, meaning that the positive rate of anti-SNRPA autoantibody was only 3.33% in the controls (P < 0.01). And anti-GFOD2 could distinguish SSc samples from both healthy and disease controls with an 8.00% sensitivity in SSc and a 98.33% specificity. Additionally, a boxplot analysis suggested that the median values of the signal intensity obtained in the SSc group were significantly higher than those obtained within the disease and healthy control groups (P < 0.001) (Figure 4). However, it is obvious that the performance of this protein obtained during Phase II validation was poorer than that observed in Phase I, suggesting that it is absolutely essential to validate candidate biomarkers with additional samples in a larger cohort (e.g., >200).

Figure 4.

Figure 4

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Scatter plot analysis of signal value for anti-SNRPA antibodies. The median values of the signal intensity obtained in the SSc group (400) were significantly higher than those obtained within the disease control (200) and healthy control groups (100) (P < 0.001). SSc = systemic sclerosis; SNRPA = small nuclear ribonucleoprotein polypeptide A; *** P < 0.001.

To best improve the performance of the autoantibodies in the diagnosis of SSc, combining the anti-SNRPA with the other clinic-used autoantibody biomarkers, mainly referring to the classic autoantibodies listed in the SSc classification criteria, were subjected to a machine learning method to calculate the area under the curve (AUC) of individual autoantibodies, and the panels. Results of AUC analysis of antibodies in the SSc group are shown in Table 1, when compared with the disease control group and the healthy control group. The AUC value of anti-SNRPA alone was 0.63 when comparing SSc patients with healthy controls and 0.54 when comparing SSc patients with disease controls. Interestingly, the anti-SNRPA could improve the AUC from 0.0043 to 0.1868 with the varies of known SSc-associated autoantibodies. In addition, SSc could be divided into four subgroups (i.e., A, B, C, and D) according to different clinical complications. Table S3 (Supporting Information) displays the results of the AUC analysis of antibodies in different subgroups of SSc when compared with the disease control group and healthy control group. Following joint detection with SNRPA, the AUC values were significantly improved, ranging from 0.0188 to 0.2809.

Table 1. Results of AUC of Different Antibodies and Antibody Combinations in the SSc Group Compared with the Control Groupa.

  SSc vs disease control SSc vs healthy control
AUC of anti-SNRPA + TOP1MT 0.6643 0.7032
AUC of anti-TOP1MT 0.6042 0.676
AUC improvement with anti-SNRPA 0.0601 0.0272
AUC of anti-SNRPA + CENPB + TOP1MT 0.7298 0.8084
AUC of anti-CENPB + TOP1MT 0.6225 0.6216
AUC improvement with anti-SNRPA 0.1073 0.1868
AUC of anti-SNRPA + CENPA + TOP1MT 0.7827 0.8541
AUC of anti-CENPA + TOP1MT 0.7579 0.8284
AUC improvement with anti-SNRPA 0.0248 0.0257
AUC of anti-SNRPA + CENPB + CENPA + TOP1MT 0.7618 0.8247
AUC of anti-CENPB + CENPA + TOP1MT 0.7321 0.7242
AUC improvement with anti-SNRPA 0.0298 0.1005
AUC of anti-SNRPA + CENPB + TOP1MT + POLR3K 0.7607 0.7583
AUC of anti-CENPB + TOP1MT + POLR3K 0.7047 0.7144
AUC improvement with anti-SNRPA 0.056 0.0439
AUC of anti-SNRPA +CENPA + TOP1MT + POLR3K 0.8196 0.841
AUC of anti-CENPA + TOP1MT + POLR3K 0.797 0.8367
AUC improvement with anti-SNRPA 0.0226 0.0043
AUC of anti-SNRPA + CENPB + CENPA + TOP1MT + POLR3K 0.7732 0.7931
AUC of anti-CENPB + CENPA + TOP1MT + POLR3K 0.7159 0.7252
AUC improvement with anti-SNRPA 0.0573 0.0678
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a

AUC = area under the curve; SSc = systemic sclerosis; SNRPA = small nuclear ribonucleoprotein polypeptide A.

Furthermore, we found that the best combinations were anti-SNRPA + CENPA + TOP1MT and anti-SNRPA + CENPA + TOP1MT + POLR3K. When comparing with the disease control group, the sensitivity and specificity of anti-SNRPA + CENPA +TOP1MT were 71.8 and 81.5%, respectively, and when compared with healthy controls, the sensitivity and specificity were 76.5 and 88.0%, respectively. When compared with disease controls, the sensitivity and specificity of anti-SNRPA + CENPA + TOP1MT + POLR3K were 69.0 and 84.0%, respectively, and compared with healthy controls, the sensitivity and specificity were 76.5 and 92.0%, respectively. These results further illustrated the contribution of anti-SNRPA in improving SSc diagnosis.

3.4. Transformation to WB Analysis

To make the detection of this novel autoantibodies (i.e., anti-SNRPA) more friendly in the clinical laboratory, it is necessary to transform these array-based tests to the classic clinically adept methods, such as western blot. SNRPA protein was re-expressed and purified with a large amount for further assay.

Based on the results obtained within the SSc-focused array in Phase II, we selected 45 anti-SNRPA positive SSc samples and 47 negative samples, including 9 SSc, 9 RA, 10 SLE, 5 SS, 10 PM/DM, and 4 healthy controls. From the results, 43 (95.6%) of the 45 array-positive serum samples were confirmed as real-positive, but two (1 pSS and 1 SLE) from the 47 array-negative samples were shown positive. Figure 5A shows representative WB results obtained with serum samples in different groups, and quantified signals for all of the tested samples are summarized in Figure 5B. The frequency of SSc diagnosis within the anti-SNRPA autoantibody positive group was significantly higher than that of the anti-SNRPA autoantibody negative group. In addition, the quantitative signals of the WB band of the anti-SNRPA antibody in the SSc group were significantly higher than those in the control group (P < 0.001). To sum up, this newly identified biomarker is credible and expected to be clinically applicable.

Figure 5.

Figure 5

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(A) Western blot validation of anti-SNRPA autoantibody. (B) Western blot signals of SSc and control groups. Based on the results obtained within the SSc-focused array in Phase II, 45 anti-SNRPA positive SSc samples and 47 negative samples, including 9 SSc, 9 RA, 10 SLE, 5 SS, 10 PM/DM, and 4 healthy controls, were selected for the western blot. SSc = systemic sclerosis; SNRPA = small nuclear ribonucleoprotein polypeptide A; PM/DM = polymyositis/dermatomyositis; SLE = systemic lupus erythematosus; RA = rheumatoid arthritis; pSS = primary Sjögren’s syndrome; HC = healthy control; WB = Western blot; *** P < 0.001.

In addition, we noticed that the SNRPA protein was acquired with an N-terminal GST tag, and anti-GST antibody in each positive serum sample should be performed to rule out the possibility that the positives were due to the presence of anti-GST antibody in the patient sera. Under the same condition, the WB results show that there seem no anti-GST signal bands, confirming that the anti-SNRPA signals were from the SSc patients, not from the anti-GST autoantibody, which might exist in some patients’ sera.

3.5. Association between Anti-SNRPA and SSc Clinical Characteristics

As shown in Table 2, the positive rate of patients with SSc with PAH in the anti-SNRPA positive group was significantly higher than that in the anti-SNRPA negative group (P < 0.001). In the anti-SNRPA positive group, the positive rate of patients with SSc, who are positive for anti-Scl-70 antibody, was significantly lower than that of the anti-SNRPA negative group (P < 0.001). In other words, the positive rate of anti-SNRPA antibody in the anti-Scl-70 antibody-negative SSc group was higher than that in the anti-Scl-70 antibody-positive SSc group, indicating that anti-SNRPA antibody is a good supplementary marker in the clinical applications of anti-Scl-70 antibody. Furthermore, Table S4 displays the results of a comparison between the laboratory parameters of the anti-SNRPA positive group and the anti-SNRPA negative group. The findings revealed that the erythrocyte sedimentation rate and immunoglobulin G levels were significantly higher in the anti-SNRPA positive patients compared with the anti-SNRPA negative patients (P < 0.05). However, there were no significant differences observed between the two groups in other laboratory test results.

Table 2. Association between Anti-SNRPA and SSc Clinical Characteristicsa.

group Anti-SNRPA positive Anti-SNRPA negative P value
total = 45 total = 355
SSc with ILD/SSc without ILD 31/5 227/88 0.07
SSc with PAH/SSc without PAH 24/16 63/239 <0.001
SSc with anti-Scl-70/SSc without anti-Scl-70 2/31 135/139 <0.001
SSc with anti-CENP/SSc without anti-CENP 1/16 43/138 0.09
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a

SNRPA = small nuclear ribonucleoprotein polypeptide A; SSc = systemic sclerosis; ILD = interstitial lung disease, PAH = pulmonary arterial hypertension.

4. Discussion

Using the 2013 ACR/EULAR SSc classification criteria, a patient score of 9 or greater confirms diagnosis as SSc, and within this scoring, autoantibody positivity accounts for 3 points. Clearly, this biomarker plays an important role in the clinical diagnosis of the disease. To expand the biomarker repertoire and improve related performance, we applied a two-phase protein array-based strategy to discover and validate novel biomarkers for SSc diagnosis. After unbiased screenings with a relatively small cohort against >20,000 human proteins on the HuProt arrays (with >75% coverage of the human proteome), 113 candidate autoantigens were discovered. During Phase II, we developed an SSc-focused array using these candidates, allowing for their validation using a much larger cohort. One protein, SNRPA, was shown to be an SSc-specific serological biomarker with excellent performance. Finally, SNRPA was confirmed as a highly specific autoantigen in patients with SSc, thus paving the way for its use within clinical applications.

Interestingly, the numbers of positive proteins identified on HuProt arrays varied more than 10-fold from sample to sample in each group, which may reflect the heterogeneity of the antigen-specific immune responses between individuals. However, the median number of positives recognized by SSc samples was significantly higher than those obtained within healthy controls. To ensure reproducibility and avoid potential overfitting problems, Phase II validation surveying a much larger cohort was necessary. Indeed, only one protein was validated with satisfactory sensitivity and specificity.

The newly validated SSc biomarker SNRPA is one of the seven Sm core proteins of the U1 snRNP complex.24 The U1 snRNP complex is involved in many basic eukaryotic cellular activities, such as pre-mRNA splicing and apoptosis. The U1 snRNP consists of U1 snRNP molecules, seven common core Sm proteins, and three specific U1 proteins [U1A (SNRPA), U1C, and U1-70 (SNRNP70)]. The U1 snRNP molecule is the main target of autoimmunity in SLE-overlap syndrome.25 SNRNP70 is the primary target antigen for anti-U1 snRNP autoantibodies and has been reported to be highly correlated with apoptosis.26 SNRPA is a 282 amino acid protein containing 2 RNA-binding domains, consisting of four antiparallel β chains and two α helices;27 its binding to U1 snRNA requires an N-terminal RNA-binding domain and several flanking amino acids.28 SNRPA associates with stem-loop II of the U1 small nuclear ribonucleoprotein, which is important for the formation of spliceosomes and the promotion of the mRNA splicing process.24 The encoded protein autoregulates itself by polyadenylation inhibition of its own pre-mRNA via dimerization and has been implicated in the coupling of splicing and polyadenylation.29,30 These functions are similar to those indicated as enriched by GO and KEGG pathway analyses, especially RNA binding and splicing, indicating that SNRPA potentially plays a crucial role in the pathogenesis of SSc.

SNRPA is moderately expressed in fat, weakly expressed in muscle, and rarely expressed in small and large intestine, spleen, liver, and lung.31 Dou et al.32 have reported that SNRPA is a potential oncogene in gastric cancer; this gene is often elevated in tumor tissues and promotes the proliferation of gastric cancer cells in vitro and in vivo. Conversely, it was reported that the presence of SNRPA seems to be favorable for cancer treatment because it can regulate the expression of a protein that promotes the survival of cancer cells.33 In addition, studies have found that SNRPA can promote alternative polyadenylation of STAT5B,34 which is a key regulator of T helper 1 (Th1) cell differentiation. Numerous studies have shown that SSc has a Th1-immune response during disease development,35,36 indirectly proving the potential role of SNRPA in the pathogenesis of SSc.

It has also been reported that anti-U1 snRNP is associated with PAH (RR 3.4, P = 0.0267).37 Similarly, in the current study, the positive rate of patients with SSc exhibiting PAH in the anti-SNRPA positive group was significantly higher than that of the anti-SNRPA negative group, and anti-SNRNP70 showed the same results (P < 0.001). Zhang et al. found that SNRPA is highly expressed in swine respiratory diseases, which supports the hypothesis that it has a role in susceptibility to respiratory diseases.38 However, some studies reported that anti-U1 snRNP was associated with reduced PAH risk.39,40 Therefore, this controversial issue requires more research to be fully addressed.

Many studies have reported that anti-U1 snRNP is common in SLE and mixed CTD,41−43 and it has also been reported to commonly correlate with SSc during overlap syndrome and strong glucocorticoid responses.44 However, the main target antigen of this antibody is SNRNP70. We identify herein a new member of the U1 snRNP complex, SNRPA, as a specific biomarker for SSc. It is worth noting that the positive rate of anti-Scl-70 antibody in both patients with SSc and the anti-SNRPA negative group was higher than that of the anti-SNRPA positive group, indicating that anti-SNRPA antibody is a good supplementary marker in the clinical applications of anti-Scl-70 antibody. Villalta et al.45 reported that SSc-associated autoantibodies were typically mutually exclusive. The relationship between anti-SNRPA and anti-Scl-70 antibodies found in the current study also supports this previous work. Indeed, we demonstrated that a combination of anti-SNRPA and anti-Scl-70 antibodies could further improve the performance compared with individual antibodies (Table 1).

In summary, this study represents the first proteome-wide survey used for the identification of autoantibodies associated with SSc employing a two-phase strategy with a large cohort of 790 subjects. We have discovered and validated SNRPA as a novel SSc-specific biomarker and emphasize the importance of it as a good additional marker with similar clinical applications as the anti-Scl-70 antibody.

Acknowledgments

This research was supported by grants from the National Key Research and Development Program of China (2018YFE0207300), supported by Beijing Natural Science Foundation (M23008), the National High Level Hospital Clinical Research Funding (2022-PUMCH-B-124).

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.3c00268.

  • Characteristics of participants in the study; a total of 113 autoantigens were found to be significantly associated with SSc; results of AUC of different antibody combinations in the different SSc subgroups compared with the control group; results of HuProt arrays quality test; distribution of positives identified within 90 serum samples (PDF)

Author Contributions

∇ C.L., G.S., S.Y., and Y.H. contributed equally to this work. C.L.: conceptualization, writing—original draft preparation; G.S.: methodology, formal analysis and investigation; S.Y.: formal analysis and investigation; Y.H.: methodology, software; C.H.: methodology; Y.H.: visualization; X.W.: writing review and editing; L.L.: writing review and editing; F.Z.: resources, investigation; H.Z.: supervision, validation; Y.L.: funding acquisition.

The authors declare no competing financial interest.

Supplementary Material

pr3c00268_si_001.pdf (191.8KB, pdf)

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