A Comparative Study on the Incidence, Aggravation, and Remission of Lupus Nephritis Based on iTRAQ Technology

iTRAQ-based mass spectrometry screen to identify serum biomarkers in systemic lupus erythematosus

OBJECTIVE

Systemic lupus erythematosus (SLE) is a complex systemic autoimmune disorder with no reliable serum biomarkers currently available other than autoantibodies.

METHODS

In the present study, isobaric tags for relative and absolute quantitation-based mass spectrometry was used to screen the sera of patients with SLE to uncover potential disease biomarkers.

RESULTS

85 common proteins were identified, with 16 being elevated (≥1.3) and 23 being decreased (≤0.7) in SLE. Of the 16 elevated proteins, serum alpha-1-microglobulin/bikunin precursor (AMBP), zinc alpha-2 glycoprotein (AZGP) and retinol-binding protein 4 (RBP4) were validated in independent cross-sectional cohorts (Cohort I, N=52; Cohort II, N=117) using an orthogonal platform, ELISA. Serum AMBP, AZGP and RBP4 were validated to be significantly elevated in both patients with inactive SLE and patients with active SLE compared with healthy controls (HCs) (p<0.05, fold change >2.5) in Cohort I. All three proteins exhibited good discriminatory power for distinguishing active SLE and inactive SLE (area under the curve=0.82-0.96), from HCs. Serum AMBP exhibited the largest fold change in active SLE (5.96) compared with HCs and correlated with renal disease activity. The elevation in serum AMBP was validated in a second cohort of patients with SLE of different ethnic origins, correlating with serum creatinine (r=0.60, p<0.001).

CONCLUSION

Since serum AMBP is validated to be elevated in SLE and correlated with renal disease, the clinical utility of this novel biomarker warrants further analysis in longitudinal cohorts of patients with lupus and lupus nephritis.

STXBP3 and GOT2 predict immunological activity in acute allograft rejection

Background

Acute allograft rejection (AR) following renal transplantation contributes to chronic rejection and allograft dysfunction. The current diagnosis of AR remains dependent on renal allograft biopsy which cannot immediately detect renal allograft injury in the presence of AR. In this study, sensitive biomarkers for AR diagnosis were investigated and developed to protect renal function.

Methods

We analyzed pre- and postoperative data from five databases combined with our own data to identify the key differently expressed genes (DEGs). Furthermore, we performed a bioinformatics analysis to determine the immune characteristics of DEGs. The expression of key DEGs was further confirmed using the real-time quantitative PCR (RT-qPCR), enzyme-linked immunosorbent assay (ELISA), and immunohistochemical (IHC) staining in patients with AR. ROC curves analysis was used to estimate the performance of key DEGs in the early diagnosis of AR.

Results

We identified glutamic-oxaloacetic transaminase 2 (GOT2) and syntaxin binding protein 3 (STXBP3) as key DEGs. The higher expression of STXBP3 and GOT2 in patients with AR was confirmed using RT-qPCR, ELISA, and IHC staining. ROC curve analysis also showed favorable values of STXBP3 and GOT2 for the diagnosis of early stage AR.

Conclusions

STXBP3 and GOT2 could reflect the immunological status of patients with AR and have strong potential for the diagnosis of early-stage AR.

Glomerular Expression of S100A8 in Lupus Nephritis: An Integrated Bioinformatics Analysis

Introduction

Lupus nephritis (LN) is a major risk factor of morbidity and mortality. Glomerular injury is associated with different pathogeneses and clinical presentations in LN patients. However, the molecular mechanisms involved are not well understood. This study aimed to explore the molecular characteristics and mechanisms of this disease using bioinformatics analysis.

Methods

To characterize glomeruli in LN, microarray datasets GSE113342 and GSE32591 were downloaded from the Gene Expression Omnibus database and analyzed to determine the differentially expressed genes (DEGs) between LN glomeruli and normal glomeruli. Functional enrichment analyses and protein–protein interaction network analyses were then performed. Module analysis was performed using the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape software. Immunofluorescence staining was performed to identify the glomerular expression of S100A8 in various International Society of Nephrology/Renal Pathology Society (ISN/RPS) class LN patients. The image of each glomerulus was acquired using a digital imaging system, and the green fluorescence intensity was quantified using Image-Pro Plus software.

Results

A total of 13 DEGs, consisting of 12 downregulated genes and one upregulated gene (S100A8), were identified in the microarray datasets. The functions and pathways associated with the DEGs mainly include inflammatory response, innate immune response, neutrophil chemotaxis, leukocyte migration, cell adhesion, cell–cell signaling, and infection. We also found that monocytes and activated natural killer cells were upregulated in both GSE113342 and GSE32591. Glomerular S100A8 staining was significantly enhanced compared to that in the controls, especially in class IV.

Conclusions

The DEGs identified in the present study help us understand the underlying molecular mechanisms of LN. Our results show that glomerular S100A8 expression varies in different pathological types; however, further research is required to confirm the role of S100A8 in LN.