Proteomics approach as a tool for salivary fingerprint characterization able to discriminate different Sjögren’s syndrome phenotype.

Background: Primary Sjögren’s syndrome (pSS) is a complex autoimmune disease mostly affecting middle-aged women. The disease is characterized by a progressive dysfunction of the salivary glands associated to a variety of systemic manifestations. Recognizing pSS might be challenging as the available subjective and objective disease biomarkers are scarcely correlated with each other and an invasive technique such as the minor salivary gland biopsy (MSGB) is often mandatory for pSS diagnosis. In addition, in the absence of novel biomarkers, it remains an unresolved issue to reach a scientific consensus on universally accepted pSS classification criteria. Aims of the study: 1) to identify and validate putative salivary biomarkers in pSS by using LC-MS/MS. 2) to highlight a specific fingerprint able to discriminate between different pSS phenotypes, defined on the basis of the focus score (FS) and on the variation of the salivary flow rate (USFR). Methods: USFR was collected from 18 patients with pSS (AECG criteria, 2002). Six patients presented a high focus score (FS≥3) and normal ESFR (group A), six patients presented a FS≥3 and an USFR<1.5 ml/15 (Group B), and 6 patients a low focus score (FS<3) and USFR <1.5 ml/15 (group C). Six healthy volunteers (HV) represented the controls. High-abundance proteins were depleted using affinity and immunodepletion methodologies. A high-throughput liquid chromatography tandem mass spectrometry (LC-MS/MS) was used for the proteomic analysis. ELISA and WB were utilized to validate preoteomics results. Results: Overall 100 differentially expressed candidate biomarkers were identified. PCA distinctively discriminate between HV and pSS patients stratified as previously described. Among the differentially expressed proteins, of interest were: cystatins, proline-rich proteins, S-100 proteins, profilin and other cell motion-related proteins, proteins involved in apoptosis, defence- and inflammatory-response. Moreover, each pSS phenotype seemed characterized by a specific proteomics fingerprint. Conclusions: The proteomics workflow was able to detect novel factors potentially related to specific phenotypes of pSS disease. These candidate biomarkers might be useful to improve pSS non-invasive diagnosis and clinical stratification.