Low-Dose Doxycycline Treatment Normalizes Levels of Some Salivary Metabolites Associated with Oral Microbiota in Patients with Primary Sjögren's Syndrome

Abstract

Saliva is a complex oral fluid, and plays a major role in oral health. Primary Sjögren's syndrome (pSS), as an autoimmune disease that typically causes hyposalivation. In the present study, salivary metabolites were studied from stimulated saliva samples (n = 15) of female patients with pSS in a group treated with low-dose doxycycline (LDD), saliva samples (n = 10) of non-treated female patients with pSS, and saliva samples (n = 14) of healthy age-matched females as controls. Saliva samples were analyzed with liquid chromatography mass spectrometry (LC-MS) based on the non-targeted metabolomics method. The saliva metabolite profile differed between pSS patients and the healthy control (HC). In the pSS patients, the LDD treatment normalized saliva levels of several metabolites, including tyrosine glutamine dipeptide, phenylalanine isoleucine dipeptide, valine leucine dipeptide, phenylalanine, pantothenic acid (vitamin B5), urocanic acid, and salivary lipid cholesteryl palmitic acid (CE 16:0), to levels seen in the saliva samples of the HC. In conclusion, the data showed that pSS is associated with an altered saliva metabolite profile compared to the HC and that the LLD treatment normalized levels of several metabolites associated with dysbiosis of oral microbiota in pSS patients. The role of the saliva metabolome in pSS pathology needs to be further studied to clarify if saliva metabolite levels can be used to predict or monitor the progress and treatment of pSS.

Keywords: Sjögren’s syndrome; doxycycline; hyposalivation; metabolomics; saliva.

Figure 1

Results of principal component analysis of the metabolomics data. In the principal component analysis (PCA), a separation of the groups can be seen, in which the metabolite profile of the saliva samples from the Sjögren’s syndrome patients before drug treatment (pSS) are separated from the metabolite profile of saliva samples from the HC (C), whereas Sjögren’s syndrome patients with LDD treatment (D) are mixed with the first two groups (A). Partial least sum of squares (PLS-DA) models between the controls and the Sjögren’s syndrome patients without drug treatment ((B): Three components, R2Y (cumulative) = 0.99, Q2 (cumulative) = 0.77), between the controls and Sjögren’s syndrome patients with low-dose doxycycline treatment ((C): Four components, R2Y (cum) = 0.99, Q2 (cum) = 0.67), and Sjögren’s syndrome patients with and without drug treatment ((D): Two components, R2Y (cum) = 0.94, Q2 (cum) = 0.39).

Figure 2

LDD treatment normalized levels of metabolites in the saliva samples of patients with Sjögren’s syndrome. Several metabolites were altered in the saliva samples of patients with pSS when compared to the saliva samples from HC. LDD treatment normalized levels of some, but not all, of these metabolites closer to levels seen in HC. Mean ion abundance with 95% confidence intervals is shown. Legend: CE 16:0, cholesteryl palmitic acid; Phe-Ile, phenylalanine isoleucine dipeptide; Tyr-Gln, Tyrosine glutamine dipeptide; Val-Leu, Valine leucine dipeptide; *, p < 0.05; **, p < 0.01; ***, p < 0.001.

Figure 3

Workflow of the non-targeted metabolomics analysis. Stimulated whole saliva (SWS) samples were collected from patients with primary Sjögren’s syndrome (pSS) before and after low-dose doxycycline (LDD) treatment (20 mg twice per day for one week) and from healthy controls. Saliva samples with low quantity or low quality were removed from the analysis. Details of the non-targeted metabolomics analysis of saliva samples has been previously described [32]. Briefly, after sample preparation, the samples were analyzed with four different analytical methods: using both reverse phase and hydrophilic interaction (HILIC) liquid chromatography (LC) separation, followed with both positive and negative electrospray ionization (ESI). After data preprocessing, we used both multivariate and univariate statistical methods to identify molecular features of interest, from which identification of metabolites was undertaken using both in-house and publicly available databases.