Skip to main content
Download PDF

Proteomic analyses reveal cystatin c is a promising biomarker for evaluation of systemic lupus erythematosus

Clinical Proteomics volume 20, Article number: 43 (2023) Cite this article

Abstract

Background

Systemic lupus erythematosus (SLE) is an autoimmune disease with multiple organ involvement, especially the kidneys. However, the underlying mechanism remains unclear, and accurate biomarkers are still lacking. This study aimed to identify biomarkers to assess organ damage and disease activity in patients with SLE using quantitative proteomics.

Methods

Proteomic analysis was performed using mass spectrometry in 15 patients with SLE and 15 age-matched healthy controls. Proteomic profiles were compared in four main subtypes: SLE with proteinuria (SLE-PN), SLE without proteinuria (SLE-non-PN), SLE with anti-dsDNA positivity (SLE-DP), and SLE with anti-dsDNA negativity (SLE-non-DP). Gene ontology biological process analysis revealed differentially expressed protein networks. Cystatin C (CysC) levels were measured in 200 patients with SLE using an immunoturbidimetric assay. Clinical and laboratory data were collected to assess their correlation with serum CysC levels.

Results

Proteomic analysis showed that upregulated proteins in both the SLE-PN and SLE-DP groups were mainly mapped to neutrophil activation networks. Moreover, CysC from neutrophil activation networks was upregulated in both the SLE-PN and SLE-DP groups. The associations of serum CysC level with proteinuria, anti-dsDNA positivity, lower complement C3 levels, and SLE disease activity index score in patients with SLE were further validated in a large independent cohort.

Conclusions

Neutrophil activation is more prominent in SLE with proteinuria and anti-dsDNA positivity, and CysC is a promising marker for monitoring organ damage and disease activity in SLE.

Introduction

Systemic lupus erythematosus (SLE) is mediated by autoreactive autoantibodies that damage multiple tissues and organs [1]. At least 50% of patients with SLE develop lupus nephritis (LN), which is a serious manifestation of SLE [2]. However, the pathogenesis of SLE remains unclear.

Proteinuria, creatinine ratio, creatinine clearance, and anti-dsDNA antibodies are common markers for the detection and assessment of LN [3]. Autoantibodies, including antinuclear antibodies (ANAs), anti-dsDNA antibodies, anti-histone antibodies, anti-SSA/Ro and anti-SSB/La antibodies, and anti-phospholipid antibodies, are involved in multiple organ damage, especially the kidneys [1]. Moreover, anti-DNA antibody levels in the serum of patients with SLE fluctuate with disease activity [4]. Anti-dsDNA antibody is the most widely used index in laboratory tests and is part of the classification criteria for SLE. They are present in nearly 80% of patients with LN and can cross-react with glomerular antigens [5]. A study showed that anti-dsDNA levels were correlated with disease severity in 40 patients with untreated LN [6]. In murine models of SLE, organ damage can be prevented or even reversed by blocking the production of pathogenic anti-dsDNA antibodies [4]. Anti-dsDNA antibodies were explored as potential therapeutic targets for SLE [7,8,9]. Proteinuria is one of the most common manifestations of kidney involvement in SLE. Currently, it is recommended that patients with overt proteinuria (e.g., urine protein: creatinine ratio > 500 mg/g) undergo kidney biopsy [10]. Proteomics-based studies have been applied in SLE to discover differentially regulated proteins [11, 12]. In some reports, C4d, serum HMGB1, and several cytokines proposed as potential biomarkers for screening SLE [13,14,15]. Although these potential biomarkers were mainly expressed in SLE, it is important to identify sensitive and specific biomarkers of renal disease activity.

In the present study, whole proteomics of patients with SLE and healthy controls was performed using mass spectrometry to identify and establish biomarkers to achieve a more accurate and reliable evaluation of renal damage and disease activity in patients with SLE.

Materials and methods

Sample collection and preparation

This study was approved by the scientific ethics committee of The First Affiliated Hospital of Anhui Medical University. Written informed consent was obtained from all participants prior to the study. Among these, 15 patients with SLE and 15 age- and sex-matched healthy controls (HCs) were used for proteomics; another 200 patients in the SLE cohort were used for serum biomarker assessment. Clinical information was abstracted from the medical records. Disease activity was recorded using the SLE disease activity index (SLEDAI) score. Patients with SLE were classified into two comparison groups: SLE patients with proteinuria (SLE-PN) versus SLE patients without proteinuria (SLE-non-PN) and SLE patients with anti-dsDNA positivity (SLE-DP) versus SLE patients with anti-dsDNA negativity (SLE-non-DP).

Preparation of peripheral blood mononuclear cells (PBMCs) and protein extraction

Blood was collected into 10-mL ethylenediaminetetraacetic acid evacuated tubes (BD, USA) for PBMC isolation. PBMCs were isolated with density-gradient centrifugation in Ficoll Paque Premium (GE Healthcare, USA) and washed twice in phosphate buffered saline. The PBMC samples were stored at − 80°C prior to use. Protein was extracted from isolated PBMCs on ice using a high-intensity ultrasonic processor (Scientz) in lysis buffer (8 M urea, 1% protease inhibitor cocktail). The remaining debris was removed by centrifugation at 12,000×g and 4 °C for 10 min. Finally, the supernatant was collected, and the protein concentration was determined using a BCA kit according to the manufacturer’s instructions.

Measurement of cystatin C (CysC) in serum samples

We collected 5 mL of peripheral blood from each individual for serum biomarker assessment. Serum was separated by centrifugation at 3000 rpm for 15 min within 2 h after sample collection. To detect serum CysC levels, enzyme-linked immunosorbent assay was performed by Jiuqiang Biotechnology (Beijing, China) in accordance with the manufacturer’s instructions.

Mass spectrometry and data analyses

In brief, the peptide was dissolved by liquid chromatography with mobile phase A and superheated by Nano Elute High performance liquid phase system for separation. Mobile phase A is an aqueous solution containing 0.1% formic acid and 2% acetonitrile. mobile phase B is a solution containing 0.1% formic acid and 100% acetonitrile. liquid phase gradient setting: 0–70 min, 4%–22%B; 70–84 min, 22% to 30%B; 84–87 min, 30%–80%B; 87–90 min, 80%B, flow rate maintained at 450.00 nL/min. The peptides are separated by an ultra-high performance liquid phase system and injected into the Capillary ion source for ionization and then analyzed by TIMES TOF Pro mass spectrometry. The ion source voltage was set to 2.0 kV, and the parent ion of the peptide segment and its secondary fragments were detected and analyzed using high-resolution TOF. The secondary mass spectrometry scan range is set to 100–1700. The data acquisition mode uses parallel cumulative serial fragmentation (PASEF) mode. A secondary spectrum with charge number of parent ions in the range of 0–5 was collected by PASEF mode for 10 times after primary mass spectrometry collection. The dynamic exclusion time of series mass spectrometry scanning was set to 30 s seconds to avoid repeated scanning of parent ions. Detailed analyses were described as previously [16]. For quality control of the expression data, we filtered low-abundance proteins (< 1 in > 80% of samples) and converted the expression data into a logarithmic form, which met the normal distribution. The Limma package (v.3.50.1) [17] was applied to define differentially expressed proteins (DEPs) between the two groups with a 1.5-fold change and P-value < 0.05. Cluster Profiler is an R package used to compare biological themes among gene clusters [18].

Statistical analyses

All statistical analyses were performed using SPSS 22.0 (IBM, Armonk, NY, USA). Continuous variables are presented as mean ± standard deviation if normally distributed (according to the Shapiro–Wilk test) and analyzed using the independent t-test; otherwise, they were presented as median (range) and analyzed using the Mann–Whitney U-test. Categorical variables are presented as n (%) and were analyzed using the chi-square test or Fisher’s exact test. Two-sided P-values < 0.05 were considered statistically significant. Student’s t-tests were used for comparisons between two groups, and one-way analysis of variance was used for multi-group comparisons using R (v.3.6.1).

Results

Patient characteristics

Patients with SLE and age- and sex-matched HCs were used for proteomics, and clinical information, including sex, age, autoantibodies, and proteinuria, was collected (Table 1). To assess the correlation between CysC and clinical and laboratory data, another cohort including 200 patients with SLE was included (Table 2). The average CysC level was 1.55 mg/L, and the average SLEDAI score was 12.55 points.

Table 1 Characteristics of patients with SLE for proteomics
Full size table
Table 2 Characteristics of patients with SLE for serum biomarker assessment
Full size table

Neutrophil activation network associated with organ damage and disease activity in SLE

Using mass spectrometry, we identified a total of 4830 proteins in the 15 HCs and 15 patients with SLE. To identify protein markers associated with disease subtypes, we performed differential protein analysis across two comparison groups: SLE-PN versus SLE-non-PN and SLE-DP versus SLE-non-DP. Indeed, 80 proteins were more abundant and 74 were less abundant at the proteomic level in the SLE-PN group than in the SLE-non-PN group, whereas 87 proteins were more abundant and 216 were less abundant in the SLE-DP group than in the SLE-non-DP group (fold change > 1.5 or < 0.67; P < 0.05; Fig. 1, Additional file 1: Data S1). Furthermore, we performed gene ontology biological process analysis of DEPs from the SLE-PN and SLE-DP groups and found that upregulated proteins in both comparison groups were mainly mapped to neutrophil activation networks (Fig. 2), including CysC, FGL2, and CD93 (Fig. 3). Previous studies reported that CysC serves as a biomarker of kidney function [19]. Here, our proteomic analysis showed that CysC was upregulated in the SLE-PN and SLE-DP groups (Fig. 4), suggesting that CysC is a biomarker of organ damage and disease activity in SLE.

Fig. 1
figure 1

Protein Expression Heterogeneity in Different Subgroups of Systemic Lupus Erythematosus (SLE). Volcano plot showing differential expression proteins (DEPs) in A SLE with proteinuria (SLE-PN) versus SLE without proteinuria (SLE-non-PN) and B SLE with anti-dsDNA positivity (SLE-DP) versus SLE without anti-dsDNA positivity (SLE-non-DP) groups. DEPs were defined as fold change > 1.5 and p < 0.05, red dots represent up-regulated proteins and  blue dots represents down-regulated proteins

Full size image
Fig. 2
figure 2

Results of GO enrichment analysis of DEPs in neutrophil activation networks in A SLE-PN versus SLE-non-PN and B SLE-DP versus SLE-non-DP groups

Full size image
Fig. 3
figure 3

Heatmap showing the expression of core proteins that contribute to neutrophil activation pathway enrichment (red, high abundance; blue, low abundance; protein names are represented by their encoding genes). A SLE-PN versus SLE-non-PN groups and HCs and B SLE-DP versus SLE-non-DP groups and healthy controls (HCs)

Full size image
Fig. 4
figure 4

Boxplot showing Cystatin C (CysC) levels in A HCs, SLE-non-PN and SLE-PN groups and B HCs, SLE-non-DP and SLE-DP groups

Full size image

CysC promote kidney damage and disease activity in SLE

To further understand the role of CysC in SLE, CysC levels were measured in 200 patients with SLE. We then evaluated the association between CysC and clinical and laboratory data of patients with SLE. Disease activity in patients with SLE was assessed using SLEDAI; a SLEDAI score of 0–9 indicated mild severity, and a SLEDAI score of > 9 indicated moderate-to-severe SLE. Logistic regression analysis was used to examine whether CysC was associated with proteinuria, hematuria, anemia, neutropenia, lower lymphopenia, complement 3 (C3) levels, anti-dsDNA, and SLEDAI scores, which revealed significant associations between CysC levels and proteinuria, anti-dsDNA, lower C3 levels, and SLEDAI scores in patients with SLE (Fig. 5A–D). We also performed a receiver operating characteristic (ROC) curves analysis to assess the CysC expression is a biomarker for SLEDAI and kidney involvement in SLE, with the areas under the ROC curves of 0.672 and 0.729, respectively (Additional file 2: Figure S1).

Fig. 5
figure 5

Boxplot showing CysC levels in A normal complement C3 versus low complement C3, B SLE-non-DP versus SLE-DP groups, and C SLE-non-PN versus SLE-PN groups. D SLE disease activity index (SLEDAI) score: mild versus severe (a score of 0–8 indicated mild severity, and a score of ≥ 9 indicated moderate-to-severe). *P < 0.05, **P < 0.01, ***P < 0.001

Full size image

Discussion

The identification of new clinical and laboratory biomarkers is crucial for early detection and supervision of disease activity and renal damage in patients with SLE. Mass spectrometry proteomics can help gain insight into protein abundance from large-scale studies of biological systems [20]. In this study, we performed whole proteomics analysis of patients with SLE and HCs using bioinformatics and biomarker validation, and CysC was evaluated as a biomarker for renal impairment and disease activity in Chinese patients with SLE.

Anti-dsDNA antibody levels have been associated with disease activity and LN [21]. Periodic measurement of anti-dsDNA antibody titers is considered essential once SLE is diagnosed to monitor disease progression [22, 23]. Additionally, accurate evaluation of proteinuria is critical to the clinical management of LN because it is currently the most important biomarker of disease activity and renal prognosis available [24, 25]. Based on these evidence, we classified patients into four subgroups: SLE-PN, SLE-non-PN, SLE-DP, SLE-non-DP and compare their proteomic difference.

There is a higher prevalence of neutrophils in patients with SLE, which are specialist cells of the innate immune system [26]. Neutrophils include normal density neutrophils (NDNs) and low-density granulocytes (LDGs). LDGs induce increased endothelial damage and vascular dysfunction in vitro, through their enhanced ability to synthesize and extrude neutrophil extracellular traps (NETs) [27]. Neutrophil transcripts were enriched in patients with active renal disease [28]. Neutrophil degranulation and activation were upregulated in active renal involvement patients with SLE [29]. In SLE, NETs stimulate the production of proinflammatory cytokines and type I interferons (IFNs), promote immune cell maturation, and contribute to tissue damage [30]. Increased numbers of apoptotic neutrophils have been found in patients with SLE and are related to anti-dsDNA antibody levels [31]. Thus, our finding of upregulated proteins in both comparison groups was mainly mapped to neutrophil activation networks with anti-dsDNA antibody positivity. A previous study also identified neutrophils/LDGs producing NETs in SLE-affected kidneys, which were correlated with anti-dsDNA antibody levels in these patients [32]. Proteins were mainly mapped to neutrophil activation networks, in addition to CysC, there are other proteins including NBEAL2, FGL2, CD93. NBEAL2 (neurobeachin-like 2) encodes a protein of 2754 amino acids and expressed in in platelets, monocytes, and neutrophils [33, 34]. Neutrophils and NK cells function abnormally in the Nbeal2-deficient immune system [35]. NBEAL2 is critically important for neutrophils as regulator of specific granule release [36]. Fibrinogen-like 2 (FGL2) is a member of the fibrinogen superfamily that exists in a membrane-bound and soluble form [37]. FGL2 expressed on neutrophils in addition to endothelial cells, macrophages and regulatory T cells (Treg) [38]. FGL2 mRNA expression is elevated in chronic kidney disease (CKD), and higher FGL2 levels are associated with fibrosis and worse outcomes [39]. Li et al. illustrated that NETs formation was regulated by the FGL2 in liver injury [40]. CD93 is a C-type lectin-like domain (CTLD) containing glycoprotein expressed on endothelial cells, platelets and a variety of leukocytes [41]. CD93 is upregulated on the surface of neutrophils upon activation in vitro, suggesting it is present in neutrophil granules [42]. Moosig et al. showed that CD93 expression was unchanged between SLE patients and controls; however, CD93 expression was inversely correlated with prednisone dosage [43]. These findings explain the association of neutrophil activation networks with organ damage and disease progression in SLE.

CysC is a member of the CysC superfamily that is encoded by the housekeeping gene CST3 and is associated with numerous immunological processes, which results in inflammatory autoimmune diseases and tumor development (multiple myeloma and breast cancer) [44, 45]. A significant association exists between high serum CysC levels, proteinuria, and reduced glomerular filtration rate, which could reflect renal damage and impaired renal function [19]. Multiple studies have shown that CysC levels are higher in patients with SLE than in controls [46,47,48]. A meta-analysis showed serum CysC is clearly superior to creatinine as a marker of GFR prediction and earlier detection of renal failure [49]. Consistently, our proteomic analysis revealed that CysC levels were upregulated in both the SLE-PN and SLE-DP groups. We also validated the significant associations between CysC level and proteinuria, anti-dsDNA antibody, lower C3 level, and SLEDAI scores in another independent cohort of patients with SLE. Reduced complement C3 and C4 protein levels and high anti-dsDNA antibody levels occur with active disease in SLE [50]. These findings suggest that CysC is a biomarker for kidney damage and disease activity in SLE. Additional routine renal function tests have identified CysC as a better marker of kidney function than creatinine due to its constant secretion and non-dependence on filtrate or other factors, such as muscular mass and sex [19, 51] and SLE is an autoimmune disease that mainly affects female patients [52]. Thus, it is thought to be a noninvasive and reliable measure to estimate kidney function.

Our study has some limitations. First, other proteins in neutrophil activation networks have not been validated in independent samples. Second, the majority of patients with SLE presented with a chronic disease, and some of them were receiving systemic therapy, which may affect CysC levels. Finally, we did not test CysC levels in a matched healthy control group.

Conclusions

Overall, our results may serve as novel and sensitive non-invasive biomarkers for renal damage and disease activity. This study adds to our understanding of neutrophil activation and CysC levels in the pathogenesis of SLE. Additionally, the study suggests that targeting CysC and key biomarkers in neutrophil activation networks may play an important role in the treatment of SLE. Future studies with larger sample sizes are required to validate the generalizability of our findings.

Availability of data and materials

The mass spectrometry proteomics data have been deposited to the Proteome Xchange Consortium via the PRIDE partner repository with the dataset identifier PXD033144.

Abbreviations

SLE:

Systemic lupus erythematosus

CysC:

Cystatin C

SLEDAI:

SLE disease activity index

SLE-PN:

Patients with proteinuria

SLE-non-PN:

Patients without proteinuria

SLE-DP:

Patients with anti-dsDNA positive

SLE-non-DP:

Patients with anti-dsDNA negative

References

  1. Su C, Johnson ME, Torres A, Thomas RM, Manduchi E. Mapping effector genes at lupus GWAS loci using promoter Capture-C in follicular helper T cells. Nat Commun. 2020;11(1):3294.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Rao DA, Arazi A, Wofsy D, Diamond B. Design and application of single-cell RNA sequencing to study kidney immune cells in lupus nephritis. Nat Rev Nephrol. 2020;16(4):238–50.

    CAS  PubMed  Google Scholar 

  3. Soliman S, Mohan C. Lupus nephritis biomarkers. Clin Immunol (Orlando, Fla). 2017;185:10–20.

    CAS  Google Scholar 

  4. Wang X, Xia Y. Anti-double Stranded DNA antibodies: origin, pathogenicity, and targeted therapies. Front Immunol. 2019;10:1667.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Franchin G, Son M, Kim SJ, Ben-Zvi I, Zhang J, Diamond B. Anti-DNA antibodies cross-react with C1q. J Autoimmun. 2013;44:34–9.

    CAS  PubMed  Google Scholar 

  6. Okamura M, Kanayama Y, Amastu K, Negoro N, Kohda S, Takeda T, et al. Significance of enzyme linked immunosorbent assay (ELISA) for antibodies to double stranded and single stranded DNA in patients with lupus nephritis: correlation with severity of renal histology. Ann Rheum Dis. 1993;52(1):14–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. VanPatten S, Sun S, He M, Cheng KF, Altiti A, Papatheodorou A, et al. Amending HIV drugs: a novel small-molecule approach to target lupus anti-DNA antibodies. J Med Chem. 2016;59(19):8859–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. He M, Cheng KF, VanPatten S, Bloom O, Diamond B, Al-Abed Y. A structural investigation of FISLE-412, a peptidomimetic compound derived from saquinavir that targets lupus autoantibodies. Bioorg Med Chem Lett. 2017;27(20):4725–9.

    CAS  PubMed  Google Scholar 

  9. Sthoeger Z, Sharabi A, Mozes E. Novel approaches to the development of targeted therapeutic agents for systemic lupus erythematosus. J Autoimmun. 2014;54:60–71.

    CAS  PubMed  Google Scholar 

  10. Fanouriakis A, Kostopoulou M, Cheema K, Anders HJ, Aringer M. 2019 update of the joint european league against rheumatism and European renal association-European dialysis and transplant association (EULAR/ERA-EDTA) recommendations for the management of lupus nephritis. Ann Rheum Dis. 2020;79(6):713–23.

    CAS  PubMed  Google Scholar 

  11. Nicolaou O, Kousios A, Hadjisavvas A, Lauwerys B, Sokratous K, Kyriacou K. Biomarkers of systemic lupus erythematosus identified using mass spectrometry-based proteomics: a systematic review. J Cell Mol Med. 2017;21(5):993–1012.

    PubMed  Google Scholar 

  12. Wang L, Dai Y, Qi S, Sun B, Wen J, Zhang L, et al. Comparative proteome analysis of peripheral blood mononuclear cells in systemic lupus erythematosus with iTRAQ quantitative proteomics. Rheumatol Int. 2012;32(3):585–93.

    PubMed  Google Scholar 

  13. Martin M, Trattner R, Nilsson SC, Björk A, Zickert A, Blom AM, et al. Plasma C4d correlates with C4d deposition in kidneys and with treatment response in lupus nephritis patients. Front Immunol. 2020;11: 582737.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. de Souza A, Westra J, Bijzet J, Limburg PC, Stegeman CA, Bijl M, et al. Is serum HMGB1 a biomarker in ANCA-associated vasculitis? Arthritis Res Ther. 2013;15(5):R104.

    PubMed  PubMed Central  Google Scholar 

  15. Reynolds JA, McCarthy EM, Haque S, Ngamjanyaporn P, Sergeant JC, Lee E, et al. Cytokine profiling in active and quiescent SLE reveals distinct patient subpopulations. Arthritis Res Ther. 2018;20(1):173.

    PubMed  PubMed Central  Google Scholar 

  16. Cai ML, Gui L, Huang H, Zhang YK, Zhang L, Chen Z, et al. Proteomic analyses reveal higher levels of neutrophil activation in men than in women with systemic lupus erythematosus. Front Immunol. 2022;13: 911997.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7): e47.

    PubMed  PubMed Central  Google Scholar 

  18. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Garcia-Garcia P, Castejon R, Tutor-Ureta P, Silvestre RA, Mellor-Pita S, Jimenez-Ortiz C, et al. Serum cystatin C is associated with kidney function but not with cardiovascular risk factors or subclinical atherosclerosis in patients with systemic lupus erythematosus. Clin Rheumatol. 2017;36(12):2709–17.

    PubMed  Google Scholar 

  20. Griss J, Perez-Riverol Y, Lewis S, Tabb DL, Dianes JA, Del-Toro N, et al. Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets. Nat Methods. 2016;13(8):651–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Infantino M, Meacci F, Bentow C, Martis P, Benucci M, Afeltra A, et al. Clinical comparison of QUANTA Flash dsDNA chemiluminescent immunoassay with four current assays for the detection of anti-dsDNA autoantibodies. J Immunol Res. 2015;2015: 902821.

    PubMed  PubMed Central  Google Scholar 

  22. Ho A, Magder LS, Barr SG, Petri M. Decreases in anti-double-stranded DNA levels are associated with concurrent flares in patients with systemic lupus erythematosus. Arthritis Rheum. 2001;44(10):2342–9.

    CAS  PubMed  Google Scholar 

  23. Biesen R, Dähnrich C, Rosemann A, Barkhudarova F, Rose T, Jakob O, et al. Anti-dsDNA-NcX ELISA: dsDNA-loaded nucleosomes improve diagnosis and monitoring of disease activity in systemic lupus erythematosus. Arthritis Res Ther. 2011;13(1):R26.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Contreras G, Pardo V, Cely C, Borja E, Hurtado A, De La Cuesta C, et al. Factors associated with poor outcomes in patients with lupus nephritis. Lupus. 2005;14(11):890–5.

    CAS  PubMed  Google Scholar 

  25. Balow JE. Clinical presentation and monitoring of lupus nephritis. Lupus. 2005;14(1):25–30.

    CAS  PubMed  Google Scholar 

  26. Hakkim A, Fürnrohr BG, Amann K, Laube B, Abed UA, Brinkmann V, et al. Impairment of neutrophil extracellular trap degradation is associated with lupus nephritis. Proc Natl Acad Sci USA. 2010;107(21):9813–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Banchereau R, Hong S, Cantarel B, Baldwin N, Baisch J, Edens M, et al. Personalized immunomonitoring uncovers molecular networks that stratify lupus patients. Cell. 2016;165(3):551–65.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Kaplan MJ. Role of neutrophils in systemic autoimmune diseases. Arthritis Res Ther. 2013;15(5):219.

    PubMed  PubMed Central  Google Scholar 

  29. Bennett L, Palucka AK, Arce E, Cantrell V, Borvak J, Banchereau J, et al. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J Exp Med. 2003;197(6):711–23.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Tay SH, Celhar T. Low-density neutrophils in systemic lupus erythematosus. Arthritis Rheumatol. 2020;72(10):1587–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Kaplan MJ. Neutrophils in the pathogenesis and manifestations of SLE. Nat Rev Rheumatol. 2011;7(12):691–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Zi M, Xu Y. Involvement of cystatin C in immunity and apoptosis. Immunol Lett. 2018;196:80–90.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Aarts CEM, Downes K, Hoogendijk AJ, Sprenkeler EGG, Gazendam RP, Favier R, et al. Neutrophil specific granule and NETosis defects in gray platelet syndrome. Blood Adv. 2021;5(2):549–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Sowerby JM, Thomas DC, Clare S, Espéli M, Guerrero JA, Hoenderdos K, et al. NBEAL2 is required for neutrophil and NK cell function and pathogen defense. J Clin Investig. 2017;127(9):3521–6.

    PubMed  PubMed Central  Google Scholar 

  35. Albers CA, Cvejic A, Favier R, Bouwmans EE, Alessi MC, Bertone P, et al. Exome sequencing identifies NBEAL2 as the causative gene for gray platelet syndrome. Nat Genet. 2011;43(8):735–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Lo RW, Li L, Leung R, Pluthero FG, Kahr WHA. NBEAL2 (Neurobeachin-Like 2) Is Required for Retention of Cargo Proteins by α-Granules During Their Production by Megakaryocytes. Arterioscler Thromb Vasc Biol. 2018;38(10):2435–47.

    CAS  PubMed  Google Scholar 

  37. Yan J, Zhao Q, Gabrusiewicz K, Kong LY, Xia X, Wang J, et al. FGL2 promotes tumor progression in the CNS by suppressing CD103+ dendritic cell differentiation. Nat Commun. 2019;10(1):448.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Rabizadeh E, Cherny I, Lederfein D, Sherman S, Binkovsky N, Rosenblat Y, et al. The cell-membrane prothrombinase, fibrinogen-like protein 2, promotes angiogenesis and tumor development. Thromb Res. 2015;136(1):118–24.

    CAS  PubMed  Google Scholar 

  39. Denicolò S, Nair V, Leierer J, Rudnicki M, Kretzler M, Mayer G, et al. Assessment of fibrinogen-like 2 (FGL2) in human chronic kidney disease through transcriptomics data analysis. Biomolecules. 2022;13(1):89.

    PubMed  PubMed Central  Google Scholar 

  40. Li X, Gao Q, Wu W, Hai S, Hu J, You J, et al. FGL2-MCOLN3-autophagy axis-triggered neutrophil extracellular traps exacerbate liver injury in fulminant viral hepatitis. Cell Mol Gastroenterol Hepatol. 2022;14(5):1077–101.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Greenlee-Wacker MC, Galvan MD, Bohlson SS. CD93: recent advances and implications in disease. Curr Drug Targets. 2012;13(3):411–20.

    CAS  PubMed  Google Scholar 

  42. Qiao N, Zhang J, Zhang Y, Liu X. Crosstalk between CD93, C1q and GIPC in the regulation of pneumococcal meningitis inflammation. Ann Clin Lab Sci. 2022;52(4):634–41.

    CAS  PubMed  Google Scholar 

  43. Moosig F, Fähndrich E, Knorr-Spahr A, Böttcher S, Ritgen M, Zeuner R, et al. C1qRP (CD93) expression on peripheral blood monocytes in patients with systemic lupus erythematosus. Rheumatol Int. 2006;26(12):1109–12.

    CAS  PubMed  Google Scholar 

  44. Leto G, Sepporta MV. The potential of cystatin C as a predictive biomarker in breast cancer. Expert Rev Anticancer Ther. 2020;20(12):1049–56.

    CAS  PubMed  Google Scholar 

  45. Jiang Y, Zhang J, Zhang C, Hong L, Jiang Y, Lu L, et al. The role of cystatin C as a proteasome inhibitor in multiple myeloma. Hematology (Amsterdam, Netherlands). 2020;25(1):457–63.

    CAS  PubMed  Google Scholar 

  46. Xu B, Zhang YM. Diagnostic performance of serum cystatin C and complement component 1q in lupus nephritis. Arthritis Res Ther. 2019;21(1):267.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Gao D, Shao J, Jin W, Xia X, Qu Y. Correlations of serum cystatin C and hs-CRP with vascular endothelial cell injury in patients with systemic lupus erythematosus. Panminerva Med. 2018;60(4):151–5.

    PubMed  Google Scholar 

  48. Chew C, Pemberton PW, Husain AA, Haque S, Bruce IN. Serum cystatin C is independently associated with renal impairment and high sensitivity C-reactive protein in systemic lupus erythematosus. Clin Exp Rheumatol. 2013;31(2):251–5.

    PubMed  Google Scholar 

  49. Dharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis. 2002;40(2):221–6.

    CAS  PubMed  Google Scholar 

  50. Yang Y, Chung EK, Zhou B, Lhotta K, Hebert LA, Birmingham DJ, et al. The intricate role of complement component C4 in human systemic lupus erythematosus. Curr Dir Autoimmun. 2004;7:98–132.

    CAS  PubMed  Google Scholar 

  51. Kar S, Paglialunga S, Islam R. Cystatin C is a more reliable biomarker for determining eGFR to support drug development studies. J Clin Pharmacol. 2018;58(10):1239–47.

    CAS  PubMed  Google Scholar 

  52. Fanouriakis A, Tziolos N, Bertsias G. Update οn the diagnosis and management of systemic lupus erythematosus. Ann Rheum Dis. 2021;80(1):14–25.

    PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This study was funded by the National Natural Science Foundation of China (81872527), Outstanding Youth Project of Natural Science Foundation of Anhui Province (2208085Y25) and National Key R&D Program of China (2022YFC3602000).

Author information

Author notes

  1. He Huang and Yukun Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China

    He Huang, Yukun Zhang, Lan Gui & Yujun Sheng

  2. Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China

    Li Zhang & Minglong Cai

  3. Department of Dermatology, China-Japan Friendship Hospital, Beijing, China

    Yujun Sheng

Authors
  1. He Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yukun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lan Gui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Minglong Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yujun Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: HH, YKZ, and YJS. Formal analysis and visualization: MLC and YKZ. Data curation: LZ, HH, and YKZ. Writing—original draft preparation: HH and YKZ. Writing—review and editing: YJS. Funding acquisition: YJS. All authors have read and agreed to the published version of the manuscript, contributed to the article, and approved the submitted version. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Minglong Cai or Yujun Sheng.

Ethics declarations

Ethics approval and consent to participate

Ethical approval was obtained from The First Affiliated Hospital of Anhui Medical University. Written informed consent was obtained from all participants prior to the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Data S1.

The differentially expressed proteins between different comparison groups.

Additional file 2: Figure S1.

ROC analysis indicates that the CysC expression is a biomarker for SLEDAI (A) and kidney involvement (B), with the areas under the ROC curves of 0.672 and 0.729, respectively.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, H., Zhang, Y., Gui, L. et al. Proteomic analyses reveal cystatin c is a promising biomarker for evaluation of systemic lupus erythematosus. Clin Proteom 20, 43 (2023). https://doi.org/10.1186/s12014-023-09434-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12014-023-09434-9

Keywords

Clinical Proteomics

ISSN: 1559-0275