Sjögren’s syndrome is the second most common autoimmune disease in the United States, affecting two to four million people1 and predominantly premenopausal women in their 40s and 50s.2 Current diagnostic techniques include a minor gland biopsy showing lymphocytic inﬁltration, the presence of oral and ocular dryness, and production of autoantibodies.3 However, there are no direct tests for the disease. Symptoms can begin months to several years before a definitive diagnosis can be made, which is frustrating for patients looking for answers.
In their publication, Ambatipudi et al.4 sought to investigate potential diagnostic biomarkers for Sjögren’s syndrome using quantitative proteomics. For their experiments, parotid saliva samples were obtained from patients with Sjögren’s syndrome and matched controls. Proteins were extracted from individual samples, and equal protein aliquots were removed from the samples and pooled together to eliminate variation between individual samples. Proteins were alkylated and reduced prior to digestion with trypsin for analysis via multidimensional protein identiﬁcation technology (MudPIT) as an initial discovery step.5
Protein digests were divided into two aliquots, for peptide/protein identiﬁcation and relative protein quantiﬁcation by spectral counting. Digested proteins were analyzed using a LTQ-Orbitrap Velos spectrometer (Thermo Scientiﬁc) coupled to an Agilent 1200 HPLC pump (Agilent).
A reverse phase (RP) separation approach was used to quantify proteins in a relatively short time, taking just 2 h compared to approximately 21 h for MudPIT analysis. The relative abundance of selected proteins detected by both the MudPIT and RP was conﬁrmed by targeted label-free quantiﬁcation.
Qualitative and quantitative spectral counts identiﬁed 1246 proteins. A total of 769 proteins (62%) were changed in abundance in comparison with matched healthy controls. Also, 477 of these proteins did not change, and 529 were only detected in either the Sjögren’s syndrome or HC sample. A total of 206 proteins were signiﬁcantly upregulated and 34 were downregulated. Ingenuity pathway analyses of differentially expressed proteins identiﬁed by MudPIT resulted in the identiﬁcation of 100 signiﬁcant pathways.
When samples were quantiﬁed in parallel, 58 of 71 proteins identiﬁed by RP overlapped with MudPIT results. Five proteins were further analyzed by targeted label-free quantiﬁcation to conﬁrm the relative differential expression. Results of that analysis revealed an upregulation of β-2-microglobulin and lactotransferrin, as well as a modest change in abundance of α-amylase. Other upregulated proteins identified included lipid-binding proteins (e.g., apolipoprotein I and II), cell adhesion proteins (e.g., CD9), and exosomes β-2-microglobulin precursor and α-enolase. Members of the cysteine proteinase inhibitor family were also identiﬁed. Although additional studies need to be undertaken using a larger patient cohort, this work has the potential to lead improved detection and monitoring of Sjögren’s syndrome.
1. Kassan, S.S., et al. (1978) ‘Increased risk of lymphoma in sicca syndrome‘, Annals of Internal Medicine, 89 (6), (pp. 888–892)
2. Pink, R., et al. (2009) ‘Saliva as a diagnostic medium‘, Biomed Papers of the Medical Facility of the University Palacky, Olomouc, Czech Republic, 153 (2), (pp. 103–110)
3. Vitali, C., et al. (2002) ‘Classiﬁcation criteria for Sjögren’s syndrome: a revised version of the European criteria proposed by the American-European Consensus Group‘, Annals of the Rheumatic Diseases, 61 (6), (pp. 554-558)
4. Ambatipudi, K.S., et al. (2012) ‘Quantitative proteomics of parotid saliva in primary Sjögren’s syndrome‘, Proteomics, 12 (19-20), (pp. 3113–3120)
5. Washburn, M.P., et al. (2001) ‘Large-scale analysis of the yeast proteome by multidimensional protein identiﬁcation technology‘, Nature Biotechnology, 19 (3), (pp. 242–247)