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165 Identification of serum biomarkers for systemic lupus erythematosus using a library of phage displayed random peptides and deep sequencing
  1. Huihua Ding1,
  2. Fan-Lin Wu2,
  3. Dan-Yun Lai3,
  4. Yuan-Jia Tang1,
  5. Zhao-Wei Xu3,
  6. Ming-Liang Ma3,
  7. Shu-Juan Guo3,
  8. Jingfang Wang3,
  9. Nan Shen1,
  10. Xiao-Dong Zhao3,
  11. Huan Qi3,
  12. Hua Li4 and
  13. Sheng-Ce Tao3
  1. 1Department of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine
  2. 2School of Agriculture, Ludong University
  3. 3Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University
  4. 4State Key laboratory for Oncogenes and Bio-ID Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai


Background Systemic lupus erythematosus (SLE) is a chronic, complex autoimmune disorder characterized by the production of autoantibodies and heterogeneous clinical presentation. Biomarkers are in urgent need for the accurate diagnosis of the disease.

Methods SLE serum autoantibodies were discovered and validated using serum samples from independent sample cohorts encompassing 306 participants divided into three groups, i.e., healthy, SLE patients, and other autoimmune diseases. To discover biomarkers for SLE, a phage displayed random peptide library (Ph.D. 12) and deep sequencing were applied to screen specific autoantibodies in a total of 100 serum samples from 50 SLE patients and 50 healthy controls. A statistical analysis protocol was setup for the identification of peptides as potential biomarkers. For validation, ten peptides were analyzed using enzyme linked immunosorbent assays (ELISA) in two independent cohorts.

Results For the screening phase, a total of 116 peptides were highly enriched by the sera of SLE patients as compared to that of the health controls. Further validation showed that using a set of four peptides panel could achieve an AUC of 0.86. Among the four peptides, two of them were further confirmed in an independent group of patients with SLE and other autoimmune diseases.

Abstract 165 Figure 1

Schematic diagram and workflow. A. The schematic for phage display screening and next generation sequencing.B.This study is composed of three major phases, i.e., screening phase, validation phase I, and validation phase II

Abstract 165 Figure 2

The candidate peptide biomarkers were discovered in the screening phase. A. The peptides which could differentiate SLE patients from healthy controls were discovered using next generation sequencing. B. Reactivity of the candidate peptides with SLE patients and health controls. C. Receiver operating characteristic curve analysis of the candidate peptides between SLE patients and health controls

Abstract 165 Figure 3

Four candidates of SLE serum biomarker were confirmed in validation phase. A. Reactivity of the serum biomarkers with sera of SLE patients and healthy controls in the ELISA validation. B. Receiver operating characteristic curve analysis of the four candidate peptides with SLE patients and health controls

Abstract 165 Figure 4

Assessment of the specificity of the four biomarkers with other related auto-immune diseases. Comparison of the SLE patients, healthy controls and other auto-immune patients, i.e., RA, ADM, BD and AS. Asterisks indicate statistical difference as compared to the SLE group (p<0.05)

Abstract 165 Figure 5

Receiver operating characteristic curve analysis using the combinational panel of the four biomarkers. The best model and the classifier for SLE against healthy controls. Based on the cross validation, a best model [(3.479 expression level of SLE2018Val001) + (13.131 expression level of SLE2018Val002) (0.051 expression level of SLE2018Val006) + (7.517 expression level of SLE2018Val008) - 10.263] was generated with the final 4 peptide panel using generalized linear model from the R language

Abstract 165 Table 1

Characteristics of study participants in the screening phase

Abstract 165 Table 2

Characteristics of study participants in the validation phase-I

Abstract 165 Table 3

Characteristics of study participants in the validation phase-II

Conclusions We demonstrate that a M13 phage displayed random peptide library in combination with deep sequencing can be used to identify peptides that could be specifically recognized by IgG in the sera of SLE patients.

Funding Source(s): N/A

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