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Open Access

Peer-reviewed

Research Article

Characteristics of gut microbiota in patients with primary Sjögren’s syndrome in Northern China

  • Yuyuan Li ,

    Contributed equally to this work with: Yuyuan Li, Zhi Li

    Roles Conceptualization, Funding acquisition, Writing – original draft

    Affiliation Advanced Institute for Medical Sciences, Dalian Medical University, Dalian, China

    ⨯
  • Zhi Li ,

    Contributed equally to this work with: Yuyuan Li, Zhi Li

    Roles Formal analysis, Funding acquisition, Methodology

    vivianmarat@163.com (ML); postlizhi@126.com (ZL)

    Affiliation Department of Clinical Laboratory, Dalian Municipal Central Hospital, Dalian, China

    ⨯
  • Wenying Sun,

    Roles Methodology

    Affiliation Department of Clinical Laboratory, Dalian Municipal Central Hospital, Dalian, China

    ⨯
  • Meiling Wang,

    Roles Methodology

    Affiliation Department of Clinical Laboratory, Dalian Municipal Central Hospital, Dalian, China

    ⨯
  • Ming Li

    Roles Conceptualization, Funding acquisition, Writing – review & editing

    vivianmarat@163.com (ML); postlizhi@126.com (ZL)

    Affiliation College of Basic Medical Science, Dalian Medical University, Dalian, China

    ⨯

Abstract

This study analyzes and compares the structure and diversity of gut microbiota in patients with primary Sjögren’s syndrome (pSS) in Northern China to healthy individuals to identify clinical features associated with dysbiosis. We included 60 Chinese pSS patients and 50 age- and gender-matched healthy controls. DNA was extracted from stool samples and subjected to 16S ribosomal RNA gene analysis (V3-V4) for intestinal dysbiosis. In addition, patients were examined for laboratory and serological pSS features. A Spearman’s correlation analysis was performed to assess correlations between individual bacteria taxa and clinical characteristics. The alpha-diversity (Chao1 and Shannon Index) and beta-diversity (unweighted UniFrac distances) of the gut microbiota differed significantly between pSS patients and healthy controls. Further analysis showed that several gut opportunistic pathogens (Bacteroides, Megamonas, and Veillonella) were significantly more abundant in pSS patients and positively correlated with their clinical indicators. In contrast, some probiotic genera (Collinsella, unidentified_Ruminococcaceae, Romboutsia, and Dorea) were significantly decreased in pSS patients and negatively correlated with their clinical indicators. Therefore, pSS patients in Northern China showed a dysbiotic intestinal microbiome enriched for potentially pathogenic genera that might be associated with autoimmune disease.

Citation: Li Y, Li Z, Sun W, Wang M, Li M (2022) Characteristics of gut microbiota in patients with primary Sjögren’s syndrome in Northern China. PLoS ONE 17(11): e0277270. https://doi.org/10.1371/journal.pone.0277270

Editor: Christopher Staley, University of Minnesota Twin Cities, UNITED STATES

Received: March 28, 2022; Accepted: October 24, 2022; Published: November 10, 2022

Copyright: © 2022 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The sequence data associated with this study are deposited in the NCBI Sequence Read Archive with the accession number PRJNA856785.

Funding: This study was supported by the National Key R&D Program (2019YFE0117700 to M.L., who conceived and designed the study), the National Natural Science Foundation of China (31900920 to M.L.), the Nature Science Foundation of Liaoning Province, China (2019-ZD-0648 to Y.L., who conceived and designed the study), the Dalian Science and Technology Innovation Project (2020JJ27SN068 to M.L.), the Dalian Medical Science Research Program (1711011 to Z.L., who carried out experiments), and Key Specialized Research Project in Liaoning Province (2022SZ005 to Z.L.). This work was also supported by Liaoning Provincial Program for Top Discipline of Basic Medical Sciences, China (M.L.).

Competing interests: The authors have declared that no competing interests exist.

Introduction

Primary Sjögren’s syndrome (pSS) is a systemic autoimmune disease with a worldwide prevalence of 0.01%-0.09%, characterized by the infiltration of leukocytes into the exocrine glands, particularly the salivary and lachrymal glands [1, 2]. It is much more common in females than in males, particularly in middle-aged women [3]. The pSS prevalence rate in China is approximately 0.33%-0.77%, depending on the diagnostic criteria used, higher than in other countries [4–6].

Its pathogenesis and etiology are poorly understood. Evidence suggests that dysbiosis of the gut microbiome contributes to the pathogenesis of several autoimmune diseases, such as inflammatory bowel disease (IBD), systemic lupus erythematosus (SLE), and rheumatoid arthritis (RA) [7–9]. Dysbiosis of the gut microbiome has also been implicated in pSS. The pilot study by de Paiva et al. [10] showed that gut dysbiosis exacerbated experimental pSS in mice and correlated with disease severity. Similarly, intestinal dysbiosis has been reported in pSS patients, associated with clinical and laboratory markers of disease activity and signs of gastrointestinal inflammation [11]. However, they did not consider or report significant differences in microbiota. Wang et al. [12] reported that the intestinal microbiota of pSS patients differed significantly among high active disease, low active disease, and control groups, especially the Streptococcu genus. Recently, Yang et al. [13] showed that the pSS gut microbiota is characterized by increased pro-inflammatory and decreased anti-inflammatory microbes. However, the correlation between differences in microbiota and clinical indicators remain unknown.

To explore the intestinal microbial balance of pSS patients in Northern China, we extracted total DNA from the fresh feces of 60 patients with RA and 50 healthy controls for 16S ribosomal RNA gene sequencing. We found differences in the gut microbiota. In addition, we correlated the gut microbiota of pSS patients with candidate biomarkers, including autoantibodies (antinuclear [ANA], anti-Ro52, anti-Ro/SSA, and anti-La/SSB) [14], rheumatoid factor (RF), C-reactive protein (CRP), immunoglobulins (Ig: IgG, IgA, IgM, and IgE), and complement components 3 (C3) and 4 (C4).

Materials and methods

Subject recruitment and clinical characteristics

We recruited 60 pSS patients aged 36–84 years from Dalian Municipal Central Hospital (Dalian, China) between January 2021 and February 2022. All pSS patients met the 2002 American-European Consensus group classification criteria [15] and the American College of Rheumatology/European League Against Rheumatism classification criteria [16]. None had used local or systemic antibiotics or probiotics in the last three months. Patients with secondary Sjögren’s syndrome, other autoimmune diseases, or gastrointestinal tract disorders were excluded. In addition, 50 age- and sex-matched healthy control individuals that met none of the pSS diagnostic criteria were enrolled in this study. The exclusion criteria for the healthy controls were: diagnosis of gut disease and use of antibiotics or probiotics within the last 3 months prior. This study was approved by the Ethics Committees of Dalian Municipal Central Hospital (Dalian, China; YN2021-002-01). All participants provided written informed consent before participation in this study.

Sampling and laboratory analyses

Serum samples were collected from all study participants and stored at -80°C until analysis. Fecal samples were obtained from 41 pSS patients and 44 healthy controls. Serum ANA was measured by the indirect immunofluorescence technique on a HEp-2 cell substrate. Serum anti-Ro52, anti-Ro/SSA, and anti-La/SSB antibody levels were determined using an enzyme-linked immunosorbent assay kit (Quintiles Laboratories North America; Marietta, GA, USA). An immunoturbidimetric assay was used to determine the serum levels of RF, CRP, IgG, IgA, IgM, IgE, C3, and C4 (Beckman Coulter, CA, USA). All analyses were performed at Dalian Municipal Central Hospital.

Genomic DNA extraction and gut microbiota sequencing

DNA was extracted from 200 mg (fresh weight) of each fecal sample using the QIAamp DNA stool Mini kit (Qiagen; Hilden, Germany) according to the manufacturer’s protocol. DNA concentration was measured and its purity was confirmed with a Nanodrop2000 (Thermo Fisher Scientific; Wilmington, CA, USA). The gut bacterial V3-V4 region was amplified using primers 341F (5’-CCTAYGGGRBGCASCAG-3’) and 806R (5’-GGACTACNNGGGTATCTAAT-3’). PCR reactions were performed with an initial denaturation step of 98°C for 2 min, 25 cycles of denaturation at 98°C for 15 s, annealing at 55°C for 30 s, and extension at 72°C for 30 s, followed by a final extension step of 72°C for 5 min before being held at 4°C. PCR amplicon sequencing was performed using the Illumina HiSeq platform at Novogene Bioinformatics Technology Co., Ltd. (Beijing, China).

Bioinformatic analysis

Sequence data analyses were mainly performed using QIIME (v.1.9.1) and R software packages (v.2.15.3). For operational taxonomic unit-based analysis, sequences were clustered using Uparse (v.7.0.1001) with a similarity cutoff of 97%. Community richness and diversity (alpha diversity) analysis were measured by Chao1 and Shannon index. Beta diversity was measured using principal coordinate analysis (PCoA) with unweighted UniFrac analysis in the R software. The linear discriminant analysis (LDA) effect size (LEfSe) analysis was used to identify differentially abundant taxa across two groups. Correlations among variables were assessed using Spearman’s rank correlation coefficient.

Statistical analysis

Continuous and normally distributed variables are presented as arithmetic means and standard error of the mean (SEM). The significance of data differences (P≤0.05) was assessed using a nonparametric t-test in GraphPad Prism 7 (Graph Pad Software; La Jolla, CA, USA). The statistical analysis of beta diversity used the nonparametric “Adonis” method in the “vegan” package of the QIIME-incorporated version of the R software. Correlation analyses used Spearman’s rank correlation tests. Statistical analyses were performed in SPSS v.9.0 (SPSS Inc.; Chicago, IL, USA).

Accession number

The sequence data associated with this study are deposited in the NCBI Sequence Read Archive with the accession number PRJNA856785.

Results

Clinical characteristics

This study enrolled 110 participants. Table 1 lists the demographic, clinical, and laboratory characteristics of the pSS patients and healthy controls. The pSS patients (n = 60) had a mean age of 59.37±1.25 years, and 95% were female. The healthy controls (n = 50) had a mean age of 60.0±1.44 years, and 90% were female. Age and sex did not differ significantly between the pSS and control participants (P>0.05). In addition, marital status, smoking status, education, occupation, and diet (including vegetarianism) did not differ significantly between the pSS and control participants (P>0.05; S1 Table). ANA, anti-SSA, anti-SSB, and anti-Ro52 positivity in pSS patients were 95.0%, 68.3%, 26.7%, 78.3%, respectively. In addition, pSS patients had significantly higher RF (P = 0.0110), IgG (P<0.0001), IgA (P = 0.0006), and C3 (P = 0.0002) levels than controls.

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Table 1. The demographic and clinical characteristics of participants.

https://doi.org/10.1371/journal.pone.0277270.t001

Comparison of the diversity of gut bacterial communities between pSS patients and healthy controls

A Venn diagram was used to visualize the composition of gut bacterial communities (Fig 1A). The numbers of genera in the pSS and control groups were 1,363 and 1,324, respectively. The total richness of genera in the two groups was 1,678. The number of genera shared by the two groups was 1,009, 60.13% of all observed genera. Next, we compared the alpha diversity in the pSS and control groups. The pSS group had higher community richness (Chao1: P = 0.0169; Fig 1B) and alpha diversity (Shannon index: P = 0.0448; Fig 1C) than the control group. Beta diversity based on an unweighted UniFrac PCoA showed the separate clustering of the pSS and control groups (Adonis test: P = 0.0010; Fig 1D), with principal components 1 and 2 accounting for 12.43% and 9.42% of the total variance, respectively.

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Fig 1. Comparison of gut microbial diversity in the pSS and control groups.

(A) Venn diagram showing the overlap of gut microbiota between pSS and control groups. (B) Chao1 richness index. (C) Shannon diversity index. (D) PCoA plots based on unweighted Unifrac distances between the pSS and control groups. Key: blue dots, control group; green dots, pSS group; *, P<0.05.

https://doi.org/10.1371/journal.pone.0277270.g001

Composition of the gut microbiota of pSS patients and healthy controls

Four phyla (Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria) accounted for >99% of the population in both of the pSS and control groups (Fig 2A). At the phylum level, we found a tendency for significantly more Proteobacteria in the pSS group (8.53%) than in the control group (5.82%; P>0.05). In addition, there were tendencies for significantly fewer Firmicutes (64.92% pSS vs. 66.90% control; P>0.05), Actinobacteria (5.89% pSS vs. 7.76% control; P>0.05), and Firmicutes/Bacteroidetes ratio (5.50 pSS vs. 7.09 control; P>0.05) in the pSS group than in the control group.

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Fig 2. Composition of gut microbiota in pSS and control groups.

(A) Phylum level. (B) Family level. (C) Genus level.

https://doi.org/10.1371/journal.pone.0277270.g002

We further studied the compositional differences in gut microbiota at the family (Fig 2B) and genus (Fig 2C) levels between groups. Nine major families (abundance ≥0.01) were identified, among which Prevotellaceae was significantly decreased and Bacteroidaceae was significantly increased, in the pSS group compared to the control group (Fig 3A). In addition, we found significant decreases in Coriobacteriaceae, Peptostreptococcaceae, Eggerthellaceae, and unidentified_Clostridiales in the pSS group compared to the control group (Fig 3A). At the genus level, 15 genera showed significant compositional changes between pSS and control groups. We found significantly increased abundances of Bacteroides, Megamonas, Veillonella, Flavonifractor, and Intestinibacter in the pSS group compared to the control group. In addition, we found significantly decreased abundances of Collinsella, unidentified_Ruminococcaceae, Romboutsia, Dorea, Fusicatenibacter, Lachnospira, Adlercreutzia, unidentified_Clostridiales, Butyricicoccus and Tyzzerella in the pSS group compared to the control group (Fig 3B).

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Fig 3. Abundances of gut microbiota in pSS and control groups.

(A) Family level. (B) Genus level.

https://doi.org/10.1371/journal.pone.0277270.g003

Screening differentially abundant gut microbiota between pSS patients and healthy controls

The LEfSe method, identified 13 significantly different components in the intestinal microbiota (LDA score>4) between the pSS and control groups. The results showed a discriminative association of order Enterobacteriales, families Bacteroidaceae and Enterobacteriaceae, genera Bacteroides and unidentified_Enterobacteriaceae, and species Escherichia_coli with the pSS group compared to the control group. In contrast, phylum Actinobacteria, class Coriobacteriia, order Coriobacteriales, families Prevotellaceae and Coriobacteriaceae, genus Collinsella, and species Collinsella_aerofaciens were significantly associated with the control group compared to the pSS group (Fig 4).

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Fig 4. LDA effect size analysis.

(A) Cladogram indicating the phylogenetic distribution of differential gut microbiota between the pSS and control groups. (B) The differential gut microbiota with an LDA Score >4 between pSS and control groups.

https://doi.org/10.1371/journal.pone.0277270.g004

Correlations between clinical characteristics and gut microbiota in pSS patients

We analyzed correlations between intestinal microbiota composition at the genus level with several clinical characteristics in pSS patients (Fig 5). The genera Bacteroides and unidentified_Prevotellaceae were positively correlated with being female. The abundances of unidentified_Enterobacteriaceae, Bacteroides, Lactobacillus, Megamonas, Streptococcus, Veillonella, unidentified_Muribaculaceae, and Barnesiella were significantly positively correlated with disease duration, positive autoantibody percentages, and RF, IgG, and IgA levels, which were all significantly higher in pSS patients than in healthy controls. Moreover, these genera were negatively correlated with C3 levels, which were significantly lower in pSS patients than in healthy controls. In contrast, the abundances of genera Klebsiella, Collinsella, unidentified_Ruminococcaceae, Romboutsia, Dorea, and Alistipes were significantly negatively correlated with disease duration, positive autoantibody percentages, and RF, IgG, and IgA levels. Moreover, these genera were positively correlated with C3 levels.

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Fig 5. Heatmap of Spearman’s correlations between intestinal microbiota composition and clinical characteristics.

Key: Red, the highest score (correlation); blue, the lowest score (correlation); *, P<0.05; **, P<0.01.

https://doi.org/10.1371/journal.pone.0277270.g005

Discussion

pSS is characterized by dryness of the mouth and eyes. Several previous studies have reported dysbiotic salivary microbiota in pSS patients [17–23]. Intestinal dysbiosis has recently been considered a possible environmental influence in pSS etiology [1–3, 24]. To our knowledge, pSS patient characteristics in Northern China have never been reported. Here, we used high throughput sequencing to assesse 50 healthy individuals and 60 pSS patients to explore the intestinal microbial balance in pSS patients in Northern China. Taxonomic analyses showed that pSS patients had lower gut microbiota diversity than healthy controls. In addition, we found microbiota differences between pSS patients and healthy controls at the family and genus levels. We first investigated the relationship between gut microbiota and candidate biomarkers, finding that gut dysbiosis was associated with clinical and laboratory pSS markers, including increased positive autoantibody percentages and RF, IgG, and IgA levels and decreased C3 levels.

Intestinal dysbiosis is observed in autoimmune diseases, including IBD, SLE, and RA, and is associated with decreased bacterial diversity, increased pro-inflammatory bacteria, and decreased anti-inflammatory bacteria [25–27]. We found significant changes in alpha- and beta-diversities between pSS patients and healthy controls, suggesting that gut microbiota in the two groups differed significantly, consistent with a previous study [24].

At the phylum level, Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria were the dominant components of gut microbiota in pSS patients and healthy controls. The proportions of these four phyla did not differ significantly between groups. Nevertheless, their changing trends in pSS patients compared to healthy controls were similar to previous studies [17, 24].

At the family level, we found a significantly lower abundance of Prevotellaceae and higher abundance of Bacteroidaceae, contrary to prior data in pSS patients [24]. However, studies have shown that patients with multiple sclerosis may have a uniform decrease in Prevotellaceae, especially the genus Prevotella [28, 29]. Similarly, a study on Chinese RA patients showed them to have decreased Prevotella [30]. The Prevotellaceae family may be beneficial for enhancing the production of protective short chain fatty acids (SCFAs), such as butyrate and propionate, which are bacterial metabolites that expand gut regulatory T cells (Tregs) [28, 31]. In addition, Scher et al. reported that Prevotella abundance was negatively associated with Bacteroides [32]. The trend in Bacteroides abundance in our study was consistant with the findings of a previous study [24]. Importantly, we observed a significant positive correlation between autoantibody positivity in pSS patients and the relative abundance of Bacteroides in fecal samples. While Bacteroides species are commensal gut bacteria and are well-known for their increasing resistance to many antibiotics (33), some have been reported to be associated with the autoimmune diseases. Davis-Richardson et al. showed that Bacteroides dorei abundance correlated positively with future autoimmunity for type 1 diabetes [33]. In addition, Bacteroides fragilis has been identified as a potential gut pathobiont in autoimmune disease [34, 35]. The increase in Bacteroides in pSS patients observed in this study may enhance disease progression. The proliferation of families Coriobacteriaceae and Eggerthellaceae (phylum Actinobacteria) is reported to be triggered by polyphenols and fibers [36] and shows significant decreases in IBD patients compared to controls [26]. Furthermore, a decrease in the beneficial bacteria Peptostreptococcaceae (genus Romboutsia; phylum Firmicutes) was observed in irritable bowel syndrome patients [37].

At the genus level, we found a significant increase in the abundance of several gut opportunistic pathogens, Bacteroides, Megamonas, Veillonella, Flavonifractor, and Intestinibacter in pSS patients. The proportions of Bacteroides, Megamonas, and Veillonella showed significant positive correlations with clinical indicators in pSS patients. Megamonas and Veillonella were enriched in chronic hepatitis B patients [38], while Flavonifractor was abundant in patients with neuromyelitis optica spectrum disorders [39], and Intestinibacter was abundant in patients with Crohn’s disease [40]. In pSS patients, we also observed decreases in some probiotic genera: Collinsella, unidentified_Ruminococcaceae, Romboutsia, Dorea, Fusicatenibacter, Lachnospira, Adlercreutzia, unidentified_Clostridiales, Butyricicoccus and Tyzzerella. The proportions of Collinsella, unidentified_Ruminococcaceae, Romboutsia, and Dorea showed significant negative correlations with clinical indicators in pSS patients. Dorea, Fusicatenibacter, Lachnospira, and Tyzzerella are all Gram-positive bacteria and belong to the Lachnospiraceae family of Firmicutes. Zhou et al. [41] reported that the abundance of Collinsella, Romboutsia, Dorea, and Fusicatenibacter were significantly lower in allergic rhinitis patients than in healthy controls. In addition, the abundance of the Dorea genus was positively correlated with SCFA concentrations [41].

Among the above differential microbiota, Veillonella had the highest correlation with the positive rate of autoantibodies. In a recent systematic review, Veillonella was found to be significantly increased in three systemic autoimmune diseases, namely SLE, RA, and SS [42]. Therefore, Veillonella may be a factor related to intestinal dysbiosis in pSS patients in Northern China. However, more studies or different approaches are needed in the near future to find the clear connection between Veillonella and pSS patients. This study had several limitations. The results were not replicated in an independent cohort, did not distinguish between men and women due to the sex imbalance in our cohort, and were obtained with subjects from a single hospital. In addition, the results were compared to healthy controls only and not patients with symptoms of dryness without a pSS diagnosis. Nevertheless, this study confirmed intestinal dysbiosis in Northern Chinese pSS patients, which deserves further investigations.

Conclusions

In summary, we confirmed that intestinal microbial diversity and composition in pSS patients differed from healthy controls in Northern China. Increased Bacteroides, Megamonas, Veillonella, Flavonifractor, and Intestinibacter, combined with decreases in Collinsella, unidentified_Ruminococcaceae, Romboutsia, Dorea, Fusicatenibacter, Lachnospira, Adlercreutzia, unidentified_Clostridiales, Butyricicoccus and Tyzzerella appear to be pSS characteristics in Northern Chinese patients. Spearman’s correlation analyses showed that Bacteroides, Megamonas, and Veillonella were positively correlated with clinical indicators in pSS patients, while Collinsella, unidentified_Ruminococcaceae, Romboutsia, and Dorea were negatively correlated. Among these differential microbiota, Veillonella may be a factor related to gut dysbiosis in pSS patients in Northern China. This study provides a theoretical basis for exploring novel diagnostic, prognostic, and treatment modalities in the new era of preventive, predictive and personalized medicine.

Supporting information

S1 Table. Demographic characteristics of the pSS patients and healthy controls.

https://doi.org/10.1371/journal.pone.0277270.s001

(DOCX)

References

  1. 1. Fox RI. Sjögren’s syndrome. Lancet. 2005 Jul 23–29;366(9482):321–31.
  2. 2. Rees F, Doherty M, Grainge MJ, Lanyon P, Zhang W. The worldwide incidence and prevalence of systemic lupus erythematosus: a systematic review of epidemiological studies. Rheumatology (Oxford). 2017 Nov 1;56(11):1945–1961. pmid:28968809
  3. 3. Qin B, Wang J, Yang Z, Yang M, Ma N, Huang F, et al. Epidemiology of primary Sjögren’s syndrome: a systematic review and meta-analysis. Ann Rheum Dis. 2015 Nov;74(11):1983–9. pmid:24938285
  4. 4. Zhang NZ, Shi CS, Yao QP, Pan GX, Wang LL, Wen ZX, et al. Prevalence of primary Sjögren’s syndrome in China. J Rheumatol. 1995 Apr;22(4):659–61. pmid:7791159
  5. 5. Narváez J, Sánchez-Fernández SÁ, Seoane-Mato D, Díaz-González F, Bustabad S. Prevalence of Sjögren’s syndrome in the general adult population in Spain: estimating the proportion of undiagnosed cases. Sci Rep. 2020 Jun 30;10(1):10627. pmid:32606345
  6. 6. Maciel G, Crowson CS, Matteson EL, Cornec D. Prevalence of Primary Sjögren’s Syndrome in a US Population-Based Cohort. Arthritis Care Res (Hoboken). 2017 Oct;69(10):1612–1616. pmid:27998024
  7. 7. Halfvarson J, Brislawn CJ, Lamendella R, Vázquez-Baeza Y, Walters WA, Bramer LM, et al. Dynamics of the human gut microbiome in inflammatory bowel disease. Nat Microbiol. 2017 Feb 13;2:17004. pmid:28191884
  8. 8. Hevia A, Milani C, López P, Cuervo A, Arboleya S, Duranti S, et al. Intestinal dysbiosis associated with systemic lupus erythematosus. mBio. 2014 Sep 30;5(5):e01548–14. pmid:25271284
  9. 9. Chen J, Wright K, Davis JM, Jeraldo P, Marietta EV, Murray J, et al. An expansion of rare lineage intestinal microbes characterizes rheumatoid arthritis. Genome Med. 2016 Apr 21;8(1):43. pmid:27102666
  10. 10. de Paiva CS, Jones DB, Stern ME, Bian F, Moore QL, Corbiere S, et al. Altered Mucosal Microbiome Diversity and Disease Severity in Sjögren Syndrome. Sci Rep. 2016 Apr 18;6:23561. pmid:27087247
  11. 11. Mandl T, Marsal J, Olsson P, Ohlsson B, Andréasson K. Severe intestinal dysbiosis is prevalent in primary Sjögren’s syndrome and is associated with systemic disease activity. Arthritis Res Ther. 2017 Oct 24;19(1):237. pmid:29065905
  12. 12. Wang X, Wang J, Guo W, Zhou Y, Sun C, Li Z, et al. [Characteristics of intestinal flora in patients with primary Sjögren syndrome]. Nan Fang Yi Ke Da Xue Xue Bao. 2020 Jul 30;40(7):949–957. Chinese. pmid:32895147
  13. 13. Yang L, Xiang Z, Zou J, Zhang Y, Ni Y, Yang J. Comprehensive Analysis of the Relationships Between the Gut Microbiota and Fecal Metabolome in Individuals With Primary Sjogren’s Syndrome by 16S rRNA Sequencing and LC-MS-Based Metabolomics. Front Immunol. 2022 May 11;13:874021. pmid:35634334
  14. 14. Martín-Nares E, Hernández-Molina G. Novel autoantibodies in Sjögren’s syndrome: A comprehensive review. Autoimmun Rev. 2019 Feb;18(2):192–198. pmid:30572138
  15. 15. Vitali C, Bombardieri S, Jonsson R, Moutsopoulos HM, Alexander EL, Carsons SE, et al. Classification criteria for Sjögren’s syndrome: a revised version of the European criteria proposed by the American-European Consensus Group. Ann Rheum Dis. 2002 Jun;61(6):554–8. pmid:12006334
  16. 16. Shiboski CH, Shiboski SC, Seror R, Criswell LA, Labetoulle M, Lietman TM, et al. 2016 American College of Rheumatology/European League Against Rheumatism classification criteria for primary Sjögren’s syndrome: A consensus and data-driven methodology involving three international patient cohorts. Ann Rheum Dis. 2017 Jan;76(1):9–16. pmid:27789466
  17. 17. van der Meulen TA, Harmsen HJM, Bootsma H, Liefers SC, Vich Vila A, Zhernakova A, et al. Dysbiosis of the buccal mucosa microbiome in primary Sjögren’s syndrome patients. Rheumatology (Oxford). 2018 Dec 1;57(12):2225–2234. pmid:30060225
  18. 18. Tseng YC, Yang HY, Lin WT, Chang CB, Chien HC, Wang HP, et al. Salivary dysbiosis in Sjögren’s syndrome and a commensal-mediated immunomodulatory effect of salivary gland epithelial cells. NPJ Biofilms Microbiomes. 2021 Mar 11;7(1):21. pmid:33707430
  19. 19. Zhou Z, Ling G, Ding N, Xun Z, Zhu C, Hua H, et al. Molecular analysis of oral microflora in patients with primary Sjögren’s syndrome by using high-throughput sequencing. PeerJ. 2018 Sep 28;6:e5649. pmid:30280027
  20. 20. Rusthen S, Kristoffersen AK, Young A, Galtung HK, Petrovski BÉ, Palm Ø, et al. Dysbiotic salivary microbiota in dry mouth and primary Sjögren’s syndrome patients. PLoS One. 2019 Jun 18;14(6):e0218319. pmid:31211815
  21. 21. Siddiqui H, Chen T, Aliko A, Mydel PM, Jonsson R, Olsen I. Microbiological and bioinformatics analysis of primary Sjogren’s syndrome patients with normal salivation. J Oral Microbiol. 2016 Oct 20;8:31119. pmid:27770517
  22. 22. Herrala M, Turunen S, Hanhineva K, Lehtonen M, Mikkonen JJW, Seitsalo H, et al. Low-Dose Doxycycline Treatment Normalizes Levels of Some Salivary Metabolites Associated with Oral Microbiota in Patients with Primary Sjögren’s Syndrome. Metabolites. 2021 Sep 3;11(9):595. pmid:34564411
  23. 23. Doaré E, Héry-Arnaud G, Devauchelle-Pensec V, Alegria GC. Healthy Patients Are Not the Best Controls for Microbiome-Based Clinical Studies: Example of Sjögren’s Syndrome in a Systematic Review. Front Immunol. 2021 Jul 29;12:699011. pmid:34394092
  24. 24. Cano-Ortiz A, Laborda-Illanes A, Plaza-Andrades I, Membrillo Del Pozo A, Villarrubia Cuadrado A, Rodríguez Calvo de Mora M, et al. Connection between the Gut Microbiome, Systemic Inflammation, Gut Permeability and FOXP3 Expression in Patients with Primary Sjögren’s Syndrome. Int J Mol Sci. 2020 Nov 19;21(22):8733.
  25. 25. Franzosa EA, Sirota-Madi A, Avila-Pacheco J, Fornelos N, Haiser HJ, Reinker S, et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat Microbiol. 2019 Feb;4(2):293–305. pmid:30531976
  26. 26. Pittayanon R, Lau JT, Leontiadis GI, Tse F, Yuan Y, Surette M, et al. Differences in Gut Microbiota in Patients With vs Without Inflammatory Bowel Diseases: A Systematic Review. Gastroenterology. 2020 Mar;158(4):930–946.e1. pmid:31812509
  27. 27. Guo M, Wang H, Xu S, Zhuang Y, An J, Su C, et al. Alteration in gut microbiota is associated with dysregulation of cytokines and glucocorticoid therapy in systemic lupus erythematosus. Gut Microbes. 2020 Nov 1;11(6):1758–1773. pmid:32507008
  28. 28. Cignarella F, Cantoni C, Ghezzi L, Salter A, Dorsett Y, Chen L, et al. Intermittent Fasting Confers Protection in CNS Autoimmunity by Altering the Gut Microbiota. Cell Metab. 2018 Jun 5;27(6):1222–1235.e6. pmid:29874567
  29. 29. Marietta E, Mangalam AK, Taneja V, Murray JA. Intestinal Dysbiosis in, and Enteral Bacterial Therapies for, Systemic Autoimmune Diseases. Front Immunol. 2020 Oct 28;11:573079. pmid:33193357
  30. 30. Chen Y, Ma C, Liu L, He J, Zhu C, Zheng F, et al. Analysis of gut microbiota and metabolites in patients with rheumatoid arthritis and identification of potential biomarkers. Aging (Albany NY). 2021 Oct 20;13(20):23689–23701. pmid:34670873
  31. 31. Bartolomaeus H, Balogh A, Yakoub M, Homann S, Markó L, Höges S, et al. Short-Chain Fatty Acid Propionate Protects From Hypertensive Cardiovascular Damage. Circulation. 2019 Mar 12;139(11):1407–1421. pmid:30586752
  32. 32. Scher JU, Sczesnak A, Longman RS, Segata N, Ubeda C, Bielski C, et al. Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. Elife. 2013 Nov 5;2:e01202. pmid:24192039
  33. 33. Davis-Richardson AG, Ardissone AN, Dias R, Simell V, Leonard MT, Kemppainen KM, et al. Bacteroides dorei dominates gut microbiome prior to autoimmunity in Finnish children at high risk for type 1 diabetes. Front Microbiol. 2014 Dec 10;5:678. pmid:25540641
  34. 34. Stewart CJ, Ajami NJ, O’Brien JL, Hutchinson DS, Smith DP, Wong MC, et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature. 2018 Oct;562(7728):583–588. pmid:30356187
  35. 35. Stewart L D M Edgar J, Blakely G, Patrick S. Antigenic mimicry of ubiquitin by the gut bacterium Bacteroides fragilis: a potential link with autoimmune disease. Clin Exp Immunol. 2018 Nov;194(2):153–165.
  36. 36. Rodríguez-Daza MC, Roquim M, Dudonné S, Pilon G, Levy E, Marette A, et al. Berry Polyphenols and Fibers Modulate Distinct Microbial Metabolic Functions and Gut Microbiota Enterotype-Like Clustering in Obese Mice. Front Microbiol. 2020 Aug 26;11:2032. pmid:32983031
  37. 37. Lee SM, Kim N, Yoon H, Kim YS, Choi SI, Park JH, et al. Compositional and Functional Changes in the Gut Microbiota in Irritable Bowel Syndrome Patients. Gut Liver. 2021 Mar 15;15(2):253–261. pmid:32457278
  38. 38. Wang J, Wang Y, Zhang X, Liu J, Zhang Q, Zhao Y, et al. Gut Microbial Dysbiosis Is Associated with Altered Hepatic Functions and Serum Metabolites in Chronic Hepatitis B Patients. Front Microbiol. 2017 Nov 13;8:2222. pmid:29180991
  39. 39. Shi Z, Qiu Y, Wang J, Fang Y, Zhang Y, Chen H, et al. Dysbiosis of gut microbiota in patients with neuromyelitis optica spectrum disorders: A cross sectional study. J Neuroimmunol. 2020 Feb 15;339:577126. pmid:31841737
  40. 40. Forbes JD, Chen CY, Knox NC, Marrie RA, El-Gabalawy H, de Kievit T, et al. A comparative study of the gut microbiota in immune-mediated inflammatory diseases-does a common dysbiosis exist? Microbiome. 2018 Dec 13;6(1):221. pmid:30545401
  41. 41. Zhou MS, Zhang B, Gao ZL, Zheng RP, Marcellin DFHM, Saro A, et al. Altered diversity and composition of gut microbiota in patients with allergic rhinitis. Microb Pathog. 2021 Dec;161(Pt A):105272. pmid:34740809
  42. 42. Gao L, Cheng Z, Zhu F, Bi C, Shi Q, Chen X. The Oral Microbiome and Its Role in Systemic Autoimmune Diseases: A Systematic Review of Big Data Analysis. Front Big Data. 2022 Jun 29;5:927520. pmid:35844967