Next Article in Journal
Mining Activities in Iron Ore Areas Have Altered the Diversity and Functional Structure of Rhizosphere Bacterial Communities in Three Crops
Previous Article in Journal
Genomic Insights into Antimicrobial Resistance and Virulence of Monophasic Salmonella enterica I 4,[5],12:i:- Isolates from Clinical and Environmental Sources in Jeollanam-do, Korea
Previous Article in Special Issue
Gut Microbiota, Microbial Metabolites, and Inflammation in Cardiac Surgery: Implications for Clinical Outcomes—A Narrative Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 13
Issue 12
10.3390/microorganisms13122730
microorganisms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Gut Microbiome in Stevens–Johnson Syndrome and Sjögren’s Disease: Correlations with Dry Eye

by
Luciana Frizon
1,*,
Talita Trevizani Rocchetti
1,
André Frizon
1,
Rafael Jorge Alves de Alcântara
1,
Cintia S. de Paiva
2 and
José Álvaro Pereira Gomes
1
1
Department of Ophthalmology and Visual Sciences, Escola Paulista de Medicina, Universidade Federal de São Paulo, Botucatu Street 822, São Paulo 04023-062, Brazil
2
Department of Ophthalmology, Baylor College of Medicine, Baylor Plz, Houston, TX 77030, USA
*
Author to whom correspondence should be addressed.
Microorganisms 2025, 13(12), 2730; https://doi.org/10.3390/microorganisms13122730 (registering DOI)
Submission received: 21 October 2025 / Revised: 18 November 2025 / Accepted: 25 November 2025 / Published: 29 November 2025
(This article belongs to the Special Issue Gut Dysbiosis in Extraintestinal and Intestinal Diseases: Novel Target for Therapeutic Approaches)
Browse Figures
Review Reports Versions Notes

Abstract

Changes in gut microbial composition may influence mucosal immune responses and contribute to systemic autoimmune manifestations. In this pilot exploratory study, we investigated and compared the gut microbiome in patients with Stevens–Johnson syndrome (SJS), patients with Sjögren’s disease (SjD), and healthy controls, using next-generation sequencing (NGS), and assessed correlations with dry eye parameters. The study included 10 patients with SJS matched by age and sex to 10 healthy controls, and 10 patients with SjD matched to an additional set of 10 healthy controls. Dry eye parameters were employed to evaluate dry eye disease (DED). Microbiome profiles were determined using next-generation sequencing of the 16S V3-V4 region and analyzed using the Silva database. The gut microbiome exhibited significant differences in the SJS group, including a reduced Chao1 index (p = 0.01) that was progressively correlated with increased ocular severity and a decrease in Faecalibacterium (p = 0.048) compared to the healthy control group. In the SJS group, strong correlations were observed between increased Christensenellaceae with decreased DED DEWS (Dry Eye Workshop score) (p = 0.04), increased Subdoligranulum with decreased NEI (National Eye Institute) score (p = 0.04), and increased Clostridia and longer TBUT (tear break-up time) (p = 0.009). In contrast, the gut microbiome of SjD patients was similar to that of healthy controls. Patients with SJS exhibited distinct alterations in gut microbial composition, characterized by reduced microbial richness and depletion of Faecalibacterium. Furthermore, a significant association was found between specific bacterial taxa and milder dry eye severity, suggesting a possible link between changes in the gut microbiome and inflammation of the ocular surface.

1. Introduction

The human microbiome, comprising trillions of microorganisms distributed across various body sites, plays a crucial role in development, immune regulation, and maintaining a physiological balance. The gut hosts the most abundant and diverse microbial population, which is shaped by diet, lifestyle, and environment [1,2]. Dysbiosis, marked by reduced microbial richness and diversity, can impair systemic homeostasis and has been linked to diseases affecting distant organs, including the eyes [1,2]. The concept of a “gut–eye axis” has emerged, implicating gut microbial imbalances in ocular inflammatory and autoimmune disorders, such as Sjögren’s disease (SjD), dry eye disease (DED), uveitis, and diabetic retinopathy [3,4,5,6].
Stevens–Johnson syndrome (SJS) and SjD are systemic autoimmune diseases with severe ocular manifestations, including persistent dry eye, that are often refractory to conventional therapies. SJS is a rare condition, with an annual prevalence of 1.2 to 6 cases per million, characterized by acute mucocutaneous inflammation and chronic cicatricial sequelae, which lead to irreversible damage to the ocular surface and eyelids, severely impairing vision [7,8]. Genetic and immune-mediated mechanisms, particularly T cell-mediated type IV hypersensitivity reactions and activation of Toll-like receptor 3, play a critical role in the recurrent mucocutaneous inflammation and severe ocular complications observed in SJS [8,9]. SjD, in contrast, primarily affects exocrine glands, contributing to dryness of the eyes and mouth, as well as systemic inflammatory manifestations. It is more prevalent, affecting 100 to 900 per million annually, with a strong female predominance [10,11,12]. Both diseases share clinical features, including severe dry eye, chronic conjunctival inflammation, and reduced salivary flow [7,10].
DED, common in both conditions, is characterized by tear film instability, hyperosmolarity, inflammation, and neurosensory abnormalities, creating a self-perpetuating inflammatory cycle [13,14]. Recent studies suggest that the gut microbiota modulates T-cell responses and contributes to ocular surface homeostasis. Animal and human models have demonstrated that alterations in the gut microbiota can exacerbate dry eye severity via immune dysregulation [15,16,17].
While gut microbiome alterations have been explored in SjD [15,18,19], and ocular surface dysbiosis has been reported in SJS [20,21,22,23], no study has examined the intestinal microbiome in SJS to date. This study is the first to characterize the gut microbiome in SJS patients and compare it with that of individuals with SjD and healthy controls while also assessing correlations with dry eye severity. We hypothesize that SJS is associated with intestinal dysbiosis, which may potentially contribute to ocular surface inflammation and the clinical severity of dry eye disease.

2. Materials and Methods

2.1. Study Design and Patient Selection

This pilot exploratory, prospective, and transversal study was approved by the Ethics Committee of the Federal University of São Paulo (approval number: 6.003.698) and conducted following the Declaration of Helsinki. All participants provided written informed consent before participation.
Participants were recruited from the Corneal and External Diseases Clinic at the Federal University of São Paulo between March 2023 and February 2024. Two independent comparison sets were analyzed: (1) ten patients with SJS matched by age and sex to ten healthy controls, and (2) ten patients with SjD matched by age and sex to a distinct group of ten healthy controls. Distinct control groups were created for each condition due to differences in age and sex distribution between the SJS and SjD patient cohorts. One participant in the SJS group was excluded after a pregnancy was identified subsequent to sample collection and microbiome processing. Therefore, the final SJS cohort included 9 patients. Participants were aged eighteen years or older, had no other ocular diseases (other than those related to SJS or dry eye), and had no history of gastrointestinal disorders. Exclusion criteria included pregnancy, infection, or surgery in the last three months, as well as ocular or systemic antibiotic and probiotic use during that period.
The SJS group included patients with chronic disease following a history of mucocutaneous inflammation induced by medications or infections, involving at least two mucous membranes. The classification into mild, moderate, and severe ocular involvement was based on the presence and extent of ocular surface sequelae, following the simplified criteria proposed initially by Sotozono et al. [24] and later adopted by Kittipibul et al. [21] (Table 1). These included eyelash abnormalities, lid margin keratinization, conjunctival inflammation, conjunctival fibrosis, limbal stem cell deficiency, corneal epitheliopathy, and corneal opacity. Patients presenting fewer or milder findings were classified as mild, those with moderate structural involvement as moderate, and those with multiple and severe complications as severe.
The SjD group consisted of patients with confirmed primary SjD, diagnosed according to the American College of Rheumatology/European League Against Rheumatism criteria [25] with no associated autoimmune comorbidities. All SjD patients reported systemic involvement and symptoms of dry eye and dry mouth, and all were receiving oral hydroxychloroquine (HCQ) at a dose of 400 mg/day, with a treatment duration of two years or longer. Three patients were receiving chronic systemic immunosuppressant therapy (methotrexate, azathioprine, or leflunomide), and one patient was using an oral corticosteroid (prednisone 20 mg). A detailed summary of all topical and systemic medications used by participants in the SJS and SjD groups is provided in the Table S1.
Healthy control subjects had no eye irritation, a tear break-up time (TBUT) ≥ 7 s, Schirmer I ≥ 10 mm, corneal fluorescein score ≤ 2, conjunctival lissamine score ≤ 2, and no meibomian gland disease. Subjects were excluded if they had prior laser-assisted in situ keratomileusis or corneal transplantation surgery, cataract surgery in the past year, punctal occlusion with plugs or cautery, a history of contact lens wear, use of topical medications other than preservative-free artificial tears, or chronic use of systemic medications known to reduce tear production.

2.2. Clinical Assessment

All participants underwent a comprehensive ophthalmologic evaluation performed by the same examiner (LF). Ocular disease involvement was assessed according to the guidelines established by the International Dry Eye Workshop (DEWS) [26]. The examination was conducted in a standardized sequence: initially, the Ocular Surface Disease Index (OSDI) questionnaire was administered, followed by the Schirmer I test, tear break-up time (TBUT), and finally, corneal fluorescein and conjunctival lissamine green staining, graded according to the National Eye Institute (NEI) scoring system. Dry eye severity was classified using the DED DEWS system [13], which ranges from 0 to 4 and is based on the cumulative assessment of clinical parameters, including OSDI, Schirmer I test, TBUT, NEI score, and subjective symptoms. Accordingly, more advanced stages of dry eye are characterized by higher DED DEWS, OSDI, and NEI scores and lower TBUT and Schirmer I values. All SJS and SjD patients had a DED DEWS ≥ 3, and the healthy control group had a score = 0.
The Schirmer I test assessed basal and reflex tear secretion over a five-minute period without the use of anesthetic eye drops [27]. TBUT was measured three times in each eye using fluorescein strips, and the average was recorded. Corneal fluorescein staining was scored across five regions (0–3 per region), with a maximum score of 15. Conjunctival staining with lissamine green was assessed over six areas, with scores from 0 to 18. The NEI score was the sum of both corneal and conjunctival staining [28].

2.3. Sample Collection

All participants received standardized stool microbiome collection kits with detailed instructions for at-home sampling. They were advised to collect the fecal sample within 48 h before the clinical visit, store it at ambient temperature, and return it on the day of the clinical examination. All samples were collected using standardized fecal microbiome collection kits (DNA/RNA ShieldTM Zymo Research Corp, Irvine, CA, USA), which contain a preservation medium capable of maintaining microbial integrity at ambient temperature for up to 30 days [29]. After clinical examination, samples were stored at ambient temperature and shipped to the laboratory (Inside Diagnósticos, São Paulo, SP, Brazil) within 48 h. Upon arrival, all samples were processed for sequencing within 2 days.

2.4. Fecal Microbiome Analysis

DNA extraction was performed using the ZymoBIOMICS™ DNA Kit (Zymo Research Corp., Irvine, CA, USA), following the manufacturer’s protocol. Amplicon sequencing of the 16S rRNA V3–V4 region was subsequently carried out using the QIAseq 16S/ITS Screening Panel (Qiagen GmbH, Hilden, Germany), according to the manufacturer’s instructions. The primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) were employed for amplification. Sequencing was carried out on the Illumina MiSeq platform using the MiSeq v2 kit (Illumina, San Diego, CA, USA). Raw sequencing reads were processed using QIIME 2 version 2024.5 [30]. Default parameters were applied for trimming and joining paired-end reads. The DADA2 plugin was used to denoise reads and generate amplicon sequence variants (ASVs), which were subsequently clustered into operational taxonomic units (OTUs) at 99% similarity using the VSEARCH plugin. Taxonomic assignment was performed based on the SILVA database (release 138.2), with taxonomic filtering to exclude mitochondria, chloroplasts, and Eukaryotic taxa [31]. The sequencing depth ranged from 45,423 to 308,901 sequences. The median value was 113,211 (Q1 = 71,588; Q3 = 204,054). Given the high sequencing depth, the samples were not rarefied in order to preserve rare sequence data in the analyses [32].

2.5. Statistical Analysis

Statistical analysis was conducted using SPSS (version 20.0, SPSS Inc., Chicago, IL, USA) for general statistical tests and RStudio (version 4.3.1) for microbiome-specific analyses. Mean comparisons between the two groups were evaluated using Student’s t-test, Fisher’s exact test, and the Mann–Whitney test. Comparison among more than two groups, analysis of variance (ANOVA), and Kruskal–Wallis tests were used. Spearman’s correlation was applied to assess linear associations between variables due to non-normal distributions [33]. In the genus-level analysis, only genera with an average relative abundance of at least 1% were included. Statistical significance was set at p = 0.05.
Microbiome data analysis and visualization were performed using the phyloseq, vegan, ggplot2, and microbiome packages in RStudio. The alpha diversity, Chao1, and Shannon indices were calculated, and statistical significance was assessed using the Mann–Whitney test with Dunn’s adjustment for multiple comparisons. Beta diversity was computed using both unweighted and weighted UniFrac distances and visualized through principal coordinate analysis (PCoA) plots. Statistical significance for beta diversity was determined using permutational multivariate analysis of variance (PERMANOVA) with the adonis and pairwise functions.

3. Results

3.1. Clinical Data

Age and sex distribution exhibited no significant differences between the SJS group and the respective healthy control group, and between the SjD group and the respective healthy control group (Table 2). The clinical examination and dry eye parameters of the SJS group and its control group, as well as the SjD group and its control group, are described in Table 3. Patients with SJS and SjD exhibited significantly worse clinical indicators, including higher OSDI and NEI scores and lower Schirmer I and TBUT values, compared to their respective healthy controls. The composite DED DEWS, which integrates all of these parameters, was also markedly higher in both disease groups (3–4), reflecting greater ocular surface involvement. All comparisons between the disease group and its control group identified statistically significant differences (p < 0.005) in dry eye parameters (Table 3).

3.2. Intestinal Microbiome Measures

Concerning alpha diversity, the Chao1 index, which represents species richness, was significantly lower in the SJS group compared to the respective healthy control group (p = 0.012) and demonstrated a progressive decline correlating with increased ocular surface severity (p = 0.01) (Figure 1A,C). In contrast, the Shannon diversity index did not show a significant difference between groups (p = 0.175) (Figure 1B). Alpha diversity comparisons between the SjD group and the respective healthy control group indicated numerical differences in the Chao1 and Shannon diversity indices, but these were not statistically significant (p = 0.181 and p = 0.377, respectively) (Figure 1A,B). Beta diversity analysis using weighted and unweighted UniFrac distances revealed no significant differences between the SJS group and its respective healthy control group (p = 0.192) or between the SjD group and its respective healthy control group (p = 0.757) (Figure 1D,E).
Table 4 shows the phylum and genus abundance of the SJS group and the respective healthy control group, and Figure 2 illustrates the phylum and genus abundance of both SJS and SjD and their respective healthy control groups. Among phyla, no statistically significant differences were observed between SJS and SjD when compared to their respective healthy control groups (Figure 2A). Regarding genus, Faecalibacterium was significantly less abundant in the SJS group compared to the respective healthy control group (p = 0.048). This result was further supported by the Hedges’ g estimate (Hedges’ g = 0.897), indicating a strong association between SJS and reduced Faecalibacterium abundance. (Figure 2B,C and Table 4). No significant differences in genus levels were found between the SjD group and the respective healthy control group (Figure 2B). Raw sequencing data and OTU tables have been deposited in the NCBI public repository and can be accessed at URL https://www.ncbi.nlm.nih.gov/bioproject/1280506 (accessed on 21 June 2025).

3.3. Dry Eye Correlations

Comparing phyla data with dry eye indices in the SJS group, Spearman’s correlation analysis revealed significant negative correlations between the NEI Score and Actinobacteriota (r = −0.695, p = 0.039) as well as Synergistota (r = −0.722, p = 0.028) (Figure 3A,B). These findings suggest that higher abundances of Actinobacteriota and Synergistota were associated with less severe dry eye symptoms, such as reduced corneal and conjunctival staining in the SJS group. In the SjD group, no statistically significant correlations were observed at the phylum level or with respect to dry eye indices.
In correlations between bacterial genera and dry eye indices in the SJS group, Spearman’s correlation revealed a moderate negative correlation between Christensenellaceae abundance and the DED DEWS (r = −0.696, p = 0.037), as well as between Subdoligranulum abundance and the NEI score (r = −0.690, p = 0.039) (Figure 3C,D). A positive correlation was also observed between Clostridia abundance and TBUT (r = 0.803, p = 0.009) (Figure 3E). These results indicate that higher abundances of Christensenellaceae, Subdoligranulum, and Clostridia are associated with less severe dry eye parameters in the SJS group. In the SjD group, a moderate negative correlation was found between Alistipes abundance and the DED DEWS (r = −0.684, p = 0.029), suggesting that a higher abundance of Alistipes was associated with less severe dry eye disease (Figure 3F). When applying FDR correction using the Benjamini–Hochberg method, only the correlation between Clostridia abundance and TBUT (p = 0.045) remained statistically significant (Table S2).
Regarding the ocular SJS group severity (mild, moderate, and severe ocular severity grades), Spearman’s correlation analysis revealed a moderate positive correlation between disease severity and the abundance of Cyanobacteria (p = 0.050, r = 0.659) and Fusobacteria (p = 0.050, r = 0.659) at the phylum level. No statistically significant correlations were observed at the genus level.
Taken together, SJS patients exhibited reduced gut microbial richness, as indicated by a lower Chao1 index and decreased abundance of Faecalibacterium, with specific bacterial taxa correlating with reduced dry eye severity.

4. Discussion

The results of this study show that patients with SJS exhibited significantly reduced gut microbial richness, including marked depletion of Faecalibacterium, compared to healthy controls. Moreover, distinct alterations in microbial composition were associated with milder dry eye parameters. To our knowledge, this is the first investigation to characterize the gut microbiome in SJS while simultaneously comparing it with patients diagnosed with primary SjD and healthy individuals.
Alpha diversity, particularly the Chao1 index, was significantly decreased in SJS patients and showed a progressive decline, possibly correlating with the severity of ocular surface inflammation. These results are consistent with prior findings in mucous membrane pemphigoid [34], a mucosal autoimmune disease that shares clinical features with SJS, including cicatrizing conjunctivitis and T cell-mediated inflammation. Similar patterns of reduced alpha diversity have been documented in uveitis [35], Behçet’s disease [36], Sjögren’s disease [15,37,38], and diabetic retinopathy [39], reinforcing the hypothesis that gut dysbiosis may play a role in immune-driven ocular pathology via systemic pathways. Reduced microbial diversity is commonly associated with increased systemic inflammation and compromised intestinal barrier function, promoting the expansion of pathogenic taxa and chronic immune activation [12,15].
A prominent finding in our cohort of SJS patients was the depletion of Faecalibacterium, a key butyrate-producing genus with well-documented anti-inflammatory properties [6]. This is consistent with reductions reported in uveitis [35], SjD [15,19], Behçet’s disease [36], and mucous membrane pemphigoid [34]. Faecalibacterium contributes to intestinal homeostasis by supporting epithelial barrier integrity, inducing tolerogenic dendritic cells and FOXP3+ regulatory T cells (Tregs), and regulating cytokine profiles. Its primary metabolite, butyrate, a short-chain fatty acid (SCFA) produced via microbial fermentation of dietary fiber, plays an essential immunomodulatory role [6]. Schaefer et al. [16,40] demonstrated that intestinal commensals, such as Faecalibacterium, can promote ocular immune tolerance by inducing Tregs in draining lymph nodes. We additionally found that the pro-inflammatory genus Prevotella was relatively more abundant in the SJS group; however, this increase was not statistically significant.
Consistent with clinical expectations, SJS patients exhibited worse dry eye outcomes, with the DED DEWS ranging from 3 to 4, and microbial diversity inversely associated with ocular surface damage. Correlations between microbiome composition and specific dry eye parameters have previously been identified in SjD [15,18]. Our study further correlated microbial composition at the phylum and genus levels with specific dry eye parameters. At the phylum level, higher abundances of Actinobacteriota and Synergistota were associated with lower NEI scores, suggesting reduced ocular surface damage. Similar findings were reported by Moon et al. [18], linking Actinobacteriota with improved TBUT and reduced dry eye severity, and Wang et al. [41] found an association between Actinobacteriota and reduced dry eye severity in Sjögren’s disease. Actinobacteria include several commensal genera with recognized anti-inflammatory and epithelial-stabilizing functions, which may contribute to improved mucosal homeostasis [18]. Although less studied, Synergistota has been linked to metabolic and immunomodulatory pathways that may influence epithelial integrity, suggesting a possible indirect protective contribution within the gut–eye axis.
At the genus level, we found that Subdoligranulum abundance was negatively correlated with NEI scores, suggesting a potentially protective role against dry eye severity. This is consistent with studies by Cao et al. [42] and Moon et al. [18], who reported depletion of Subdoligranulum in both SjD and non-Sjögren dry eye, supporting its association with a healthier gut environment through butyrate production. Increased abundance of Christensenellaceae was also correlated with a lower DED DEWS. Members of this family are considered beneficial commensals linked to anti-inflammatory effects, improved epithelial integrity, and lower systemic inflammation [43]. Finally, Clostridia, another SCFA-producing group, was positively associated with TBUT, aligning with evidence that Clostridia enhances regulatory immune responses and mucosal barrier function [44]. Together, these taxa may support ocular surface homeostasis through gut-derived immunomodulatory mechanisms relevant to the gut–eye axis.
In our study, alpha- and beta-diversity in the SjD group did not differ significantly from those of healthy controls, in agreement with findings reported by Mendez et al. [19], Moon et al. [18], and Zhang et al. [45], but in contrast to the decreased microbial diversity described by Schaefer et al. [17], de Paiva et al. [15], and Cano-Ortiz et al. [38]. Similarly, microbial composition at both the phylum and genus levels showed no statistically significant differences in our cohort. However, a recent study by Jia et al. [46] using shotgun metagenomic sequencing in treatment-naïve primary SjD revealed marked compositional and functional aberrations in the gut microbiota, characterized by reduced microbial richness along with enrichment of potentially pro-inflammatory taxa.
An important consideration in interpreting the microbiome findings of the SjD group is that all patients were under oral hydroxychloroquine (HCQ) treatment for two years or longer. HCQ has been shown to modulate gut microbial composition in autoimmune diseases, partially reversing dysbiosis and reducing pro-inflammatory bacterial signatures in patients with primary Sjögren’s syndrome [47]. Reviews of gut microbiota–drug interactions also indicate that HCQ can alter overall microbial diversity, shift the abundance of key commensal taxa, and influence host immune responses [48]. Experimental models further support these effects, demonstrating that HCQ reduces diversity and induces measurable changes in microbial composition [49,50]. Thus, the relative similarity observed between the SjD and healthy control groups in our study may reflect treatment-related modulation of the microbiome rather than an absence of disease-associated alterations. Hydroxychloroquine should therefore be considered an important confounding factor, underscoring the need for future studies including medication-naïve patients or stratified analyses.
The use of additional systemic medications as immunosuppressants in the SjD group (such as methotrexate, azathioprine, leflunomide, and oral corticosteroids) may have modulated the gut microbial composition and attenuated disease-related differences. Likewise, the use of topical anti-inflammatory therapies in SJS patients, including corticosteroids and tacrolimus, may have improved ocular surface signs and reduced the strength of the microbiome–clinical associations. These factors represent potential confounders and should be considered when interpreting our findings.
Despite its strengths, this study has several limitations. It is an exploratory pilot investigation with a cross-sectional, single-center design and a relatively small cohort, particularly for a rare condition such as SJS, for which patient recruitment is inherently constrained. Dietary habits were not assessed, which represents an important uncontrolled confounding factor given the strong influence of diet on gut microbial composition. The absence of longitudinal follow-up further limits the ability to draw temporal or causal inferences. Moreover, the 16S rRNA sequencing approach used provides genus-level resolution and does not allow species-level or functional gene-level characterization. Therefore, the findings should be interpreted as exploratory and hypothesis-generating rather than confirmatory. Finally, because the study population was drawn from a single center, the results may not be generalizable to other populations with different genetic backgrounds, dietary patterns, or environmental exposures.
Future studies using shotgun metagenomics, strain-resolved profiling, and integration of host immune and metabolomic data, including assessment of SCFA synthesis pathways and immunomodulatory gene content, are warranted to enhance mechanistic insights.

5. Conclusions

In summary, our findings indicate that patients with SJS exhibit reduced gut microbial richness, including a depletion of anti-inflammatory taxa such as Faecalibacterium. Moreover, specific taxonomic groups were correlated with milder dry eye parameters. These results provide preliminary evidence supporting the potential role of the gut microbiome in ocular surface inflammation. Although these associations should be interpreted with caution, given the cross-sectional design, small sample size, and interindividual variability, this study provides novel insights into the gut–eye axis and expands the growing understanding of microbiome involvement in ocular immunopathology and SJS-related dry eye. These findings highlight the need for larger, longitudinal studies with adequate statistical power and robust effect-size estimation, ideally integrating functional metagenomics and immunological profiling to validate these associations and to advance microbiome-based therapeutic strategies for SJS-associated dry eye.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms13122730/s1, Table S1: Summary of topical and systemic medications reported by participants at the time of sample collection. SJS, Stevens-Johnson syndrome; SjD, Sjögren’s disease, HCQ, hydroxychloroquine 400mg/day (years of use). Table S2: Spearman’s correlation without adjustment and with FDR correction using the Benjamini–Hochberg method, between dry eye indices and intestinal microbiome. In SJS, the phyla Actinobacteriota and Synergistota revealed significant negative correlations with NEI Score (r = −0.695, p = 0.038 and r = −0.722, p = 0.028, respectively). The genera Christensenellaceae revealed a significant negative correlation with DED DEWS (r = −0.696, p = 0.037), as well as Subdoligranulum abundance and the NEI Score (r = −0.690, p = 0.039). A positive correlation was also observed between Clostridia abundance and TBUT (r = 0.803, p = 0.009). In the SjD group, a moderate negative correlation was found between Alistipes abundance and the DED DEWS (r = −0.684, p = 0.029). When applying FDR correction using the Benjamini–Hochberg method, only the correlation between Clostridia abundance and TBUT (p = 0.045) remained statistically significant. NEI score, cornea fluorescein and conjunctival lissamine green dye staining; DED DEWS, dry eye disease Dry Eye Workshop score; TBUT, tear break-up time.

Author Contributions

L.F. wrote the original draft and made subsequent revisions; T.T.R. contributed to the methodology and writing of the original draft, and reviewing and editing the final manuscript; A.F. contributed to the writing, reviewing, and editing of the final manuscript; R.J.A.d.A. contributed to the methodology and reviewing and editing the final manuscript, C.S.d.P. contributed to the analysis and reviewing and editing the final manuscript; J.Á.P.G. contributed to the design of the study and reviewing and editing the final manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

No external funding was received for this research. C.S.d.P. receives salary support from the Caroline Elles Professorship.

Institutional Review Board Statement

In this study, we adhered to the ethical standards of the Declaration of Helsinki. The study was approved by the Ethics Committee of the Federal University of São Paulo (approval number: 6.003.698, approval date: 14 April 2023). Informed consent was obtained from all subjects before enrolment.

Informed Consent Statement

Informed consent was obtained from all subjects before enrolment.

Data Availability Statement

Publicly available datasets were analyzed in this study. Raw sequencing data and OTU tables have been deposited in the NCBI public repository and can be accessed at URL https://www.ncbi.nlm.nih.gov/bioproject/1280506 (accessed on 21 June 2025).

Acknowledgments

The authors would like to thank Humberto Bezerra de Araújo Filho for his valuable contribution to the bioinformatic analyses and scientific discussion.

Conflicts of Interest

Outside of any subjects addressed in the present work, José Álvaro Pereira Gomes has the following financial interests or relationships to disclose: Alcon Laboratories, Inc.: Consultant/Advisor, Lecture Fees/Speakers Bureau, Grant Support; Allergan Medical Affairs/Abbvie: Consultant/Advisor, Lecture Fees/Speakers Bureau; Bausch + Lomb: Consultant/Advisor, Lecture Fees/Speakers Bureau; CAPES: Grant Support; Celebrim: Consultant/Advisor, Lecture Fees/Speakers Bureau; Cnpq: Grant Support; EMS: Consultant/Advisor; Fapesp: Grant Support; Genon: Lecture Fees/Speakers Bureau; Johnson & Johnson: Consultant/Advisor, Lecture Fees/Speakers Bureau; Latinofarma/Cristália: Consultant/Advisor, Lecture Fees/Speakers Bureau, Grant Support; Mediphacos: Consultant/Advisor; Novartis: Consultant/Advisor; Ofta: Consultant/Advisor, Lecture Fees/Speakers Bureau, Grant Support. Cintia S. de Paiva has the following financial interests or relationships to disclose: HannAll: research grant. All other authors declare no conflicts of interest.

References

  1. The Human Microbiome Project Consortium. A framework for human microbiome research. Nature 2012, 486, 215–221. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  2. Aagaard, K.; Petrosino, J.; Keitel, W.; Watson, M.; Katancik, J.; Garcia, N.; Patel, S.; Cutting, M.; Madden, T.; Hamilton, H.; et al. The Human Microbiome Project strategy for comprehensive sampling of the human microbiome and why it matters. FASEB J. 2013, 27, 1012–1022. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  3. Zárate-Bladés, C.R.; Horai, R.; Mattapallil, M.J.; Ajami, N.J.; Wong, M.; Petrosino, J.F.; Itoh, K.; Chan, C.-C.; Caspi, R.R. Gut microbiota as a source of a surrogate antigen that triggers autoimmunity in an immune-privileged site. Gut Microbes. 2017, 8, 59–66. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  4. Wang, C.; Schaefer, L.; Bian, F.; Yu, Z.; Pflugfelder, S.C.; Britton, R.A.; de Paiva, C.S. Dysbiosis Modulates Ocular Surface Inflammatory Response to Liposaccharide. Investig. Ophthalmol. Vis. Sci. 2019, 60, 4224–4233. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  5. Campagnoli, L.I.M.; Varesi, A.; Barbieri, A.; Marchesi, N.; Pascale, A. Targeting the Gut-Eye Axis: An Emerging Strategy to Face Ocular Diseases. Int. J. Mol. Sci. 2023, 24, 13338. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  6. Trujillo-Vargas, C.M.; Schaefer, L.; Alam, J.; Pflugfelder, S.C.; Britton, R.A.; de Paiva, C.S. The gut-eye-lacrimal gland-microbiome axis in Sjögren Syndrome. Ocul. Surf. 2020, 18, 335–344. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  7. Kohanim, S.; Palioura, S.; Saeed, H.N.; Akpek, E.K.; Amescua, G.; Basu, S.; Blomquist, P.H.; Bouchard, C.S.; Dart, J.K.; Gai, X.; et al. Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis—A Comprehensive Review and Guide to Therapy. I. Systemic Disease. Ocul. Surf. 2016, 14, 2–19. [Google Scholar] [CrossRef] [PubMed]
  8. Ueta, M. Pathogenesis of Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis with Severe Ocular Complications. Front. Med. 2021, 8, 651247. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  9. Ueta, M.; Sotozono, C.; Inatomi, T.; Kojima, K.; Tashiro, K.; Hamuro, J.; Kinoshita, S. Toll-like receptor 3 gene polymorphisms in Japanese patients with Stevens-Johnson syndrome. Br. J. Ophthalmol. 2007, 91, 962–965. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  10. Mariette, X.; A Criswell, L.A. Primary Sjögren’s Syndrome. N. Engl. J. Med. 2018, 378, 931–939. [Google Scholar] [CrossRef] [PubMed]
  11. Ramos-Casals, M.; Baer, A.N.; Brito-Zerón, M.d.P.; Hammitt, K.M.; Bouillot, C.; Retamozo, S.; Mackey, A.; Yarowsky, D.; Turner, B.; Blanck, J.; et al. 2023 International Rome consensus for the nomenclature of Sjögren disease. Nat. Rev. Rheumatol. 2025, 21, 426–437. [Google Scholar] [CrossRef] [PubMed]
  12. de Paiva, C.S.; Pflugfelder, S.C. Mechanisms of Disease in Sjögren Syndrome-New Developments and Directions. Int. J. Mol. Sci. 2020, 21, 650. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  13. Craig, J.P.; Nichols, K.K.; Akpek, E.K.; Caffery, B.; Dua, H.S.; Joo, C.-K.; Liu, Z.; Nelson, J.D.; Nichols, J.J.; Tsubota, K.; et al. TFOS DEWS II Definition and Classification Report. Ocul. Surf. 2017, 15, 276–283. [Google Scholar] [CrossRef]
  14. Pflugfelder, S.C.; de Paiva, C.S. The Pathophysiology of Dry Eye Disease: What We Know and Future Directions for Research. Ophthalmology 2017, 124, S4–S13. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  15. de Paiva, C.S.; Jones, D.B.; Stern, M.E.; Bian, F.; Moore, Q.L.; Corbiere, S.; Streckfus, C.F.; Hutchinson, D.S.; Ajami, N.J.; Petrosino, J.F.; et al. Altered Mucosal Microbiome Diversity and Disease Severity in Sjögren Syndrome. Sci. Rep. 2016, 6, 23561. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  16. Zaheer, M.; Wang, C.; Bian, F.; Yu, Z.; Hernandez, H.; de Souza, R.G.; Simmons, K.T.; Schady, D.; Swennes, A.G.; Pflugfelder, S.C.; et al. Protective role of commensal bacteria in Sjögren Syndrome. J. Autoimmun. 2018, 93, 45–56. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  17. Schaefer, L.; Trujillo-Vargas, C.M.; Midani, F.S.; Pflugfelder, S.C.; Britton, R.A.; de Paiva, C.S. Gut Microbiota from Sjögren syndrome Patients Decreased T Regulatory Cells in the Lymphoid Organs and Desiccation-Induced Corneal Barrier Disruption in Mice. Front. Med. 2022, 9, 852918. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  18. Moon, J.; Choi, S.H.; Yoon, C.H.; Kim, M.K. Gut dysbiosis is prevailing in Sjögren’s syndrome and is related to dry eye severity. PLoS ONE 2020, 15, e0229029. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  19. Mendez, R.; Watane, A.; Farhangi, M.; Cavuoto, K.M.; Leith, T.; Budree, S.; Galor, A.; Banerjee, S. Gut microbial dysbiosis in individuals with Sjögren’s syndrome. Microb. Cell Fact. 2020, 19, 90. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  20. Frizon, L.; Araújo, M.C.; Andrade, L.; Yu, M.C.Z.; Wakamatsu, T.H.; Höfling-Lima, A.L.; Gomes, J.Á.P. Evaluation of conjunctival bacterial flora in patients with Stevens-Johnson Syndrome. Clinics 2014, 69, 168–172. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  21. Kittipibul, T.; Puangsricharern, V.; Chatsuwan, T. Comparison of the ocular microbiome between chronic Stevens-Johnson syndrome patients and healthy subjects. Sci. Rep. 2020, 10, 4353. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  22. Singh, S.; Maity, M.; Shanbhag, S.; Arunasri, K.; Basu, S. Lid Margin Microbiome in Stevens-Johnson Syndrome Patients with Lid Margin Keratinization and Severe Dry Eye Disease. Investig. Ophthalmol. Vis. Sci. 2024, 65, 28. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  23. Frizon, L.; Rocchetti, T.T.; Frizon, A.; de Alcântara, R.J.A.; Hofling-Lima, A.L.; de Paiva, C.S.; Gomes, J.Á.P. Ocular bacterial microbiome analysis by next-generation sequencing in patients with Stevens-Johnson syndrome and Sjögren’s disease: Associations with dry eye indices. Exp. Eye. Res. 2025, 260, 110622. [Google Scholar] [CrossRef] [PubMed]
  24. Sotozono, C.; Ang, L.P.; Koizumi, N.; Higashihara, H.; Ueta, M.; Inatomi, T.; Yokoi, N.; Kaido, M.; Dogru, M.; Shimazaki, J.; et al. New grading system for the evaluation of chronic ocular manifestations in patients with Stevens-Johnson syndrome. Ophthalmology 2007, 114, 1294–1302. [Google Scholar] [CrossRef]
  25. Shiboski, C.H.; Shiboski, S.C.; Seror, R.; Criswell, L.A.; Labetoulle, M.; Lietman, T.M.; Rasmussen, A.; Scofield, H.; Vitali, C.; Bowman, S.J.; et al. International Sjögren’s Syndrome Criteria Working Group. 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. Arthritis Rheumatol. 2017, 69, 35–45. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  26. DEWS. The definition and classification of dry eye disease: Report of the Definition and Classification Subcommittee of the International Dry Eye WorkShop (2007). Ocul. Surf. 2007, 5, 75–92. [Google Scholar] [CrossRef] [PubMed]
  27. Jones, L.T. The lacrimal secretory system and its treatment. Am. J. Ophthalmol. 1966, 62, 47–60. [Google Scholar] [CrossRef] [PubMed]
  28. Bron, A.J.F.; Evans, V.E.B.; Smith, J.A. Grading of corneal and conjunctival staining in the context of other dry eye tests. Cornea 2003, 22, 640–650. [Google Scholar] [CrossRef] [PubMed]
  29. Kazantseva, J.; Malv, E.; Kaleda, A.; Kallastu, A.; Meikas, A. Optimisation of sample storage and DNA extraction for human gut microbiota studies. BMC Microbiol. 2021, 21, 158. [Google Scholar] [CrossRef]
  30. Bolyen, E.; Rideout, J.R.; Dillon, M.R.; Bokulich, N.A.; Abnet, C.C.; Al-Ghalith, G.A.; Alexander, H.; Alm, E.J.; Arumugam, M.; Asnicar, F.; et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 2019, 37, 852–857, Erratum in Nat. Biotechnol. 2019, 37, 1091. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  31. Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glöckner, F.O. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013, 41, D590–D596. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  32. McMurdie, P.J.; Holmes, S. Waste not, want not: Why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 2014, 10, e1003531. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  33. Bussab, W.O.; Morettin, P.A. Estatística Básica, 10th ed.; Saraiva Educação: São Paulo, Brasil, 2021. [Google Scholar]
  34. Low, L.; Suleiman, K.; Shamdas, M.; Bassilious, K.; Poonit, N.; Rossiter, A.E.; Acharjee, A.; Loman, N.; Murray, P.I.; Wallace, G.R.; et al. Gut Dysbiosis in Ocular Mucous Membrane Pemphigoid. Front. Cell. Infect. Microbiol. 2022, 12, 780354. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  35. Chakravarthy, S.K.; Jayasudha, R.; Prashanthi, G.S.; Ali, M.H.; Sharma, S.; Tyagi, M.; Shivaji, S. Dysbiosis in the Gut Bacterial Microbiome of Patients with Uveitis, an Inflammatory Disease of the Eye. Indian J. Microbiol. 2018, 58, 457–469. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  36. Ye, Z.; Zhang, N.; Wu, C.; Zhang, X.; Wang, Q.; Huang, X.; Du, L.; Cao, Q.; Tang, J.; Zhou, C.; et al. A metagenomic study of the gut microbiome in Behcet’s disease. Microbiome 2018, 6, 135. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  37. van der Meulen, T.A.; Harmsen, H.J.; Vila, A.V.; Kurilshikov, A.; Liefers, S.C.; Zhernakova, A.; Fu, J.; Wijmenga, C.; Weersma, R.K.; de Leeuw, K.; et al. Shared gut, but distinct oral microbiota composition in primary Sjögren’s syndrome and systemic lupus erythematosus. J. Autoimmun. 2019, 97, 77–87. [Google Scholar] [CrossRef] [PubMed]
  38. Cano-Ortiz, A.; Laborda-Illanes, A.; Plaza-Andrades, I.; del Pozo, A.M.; Cuadrado, A.V.; de Mora, M.R.C.; Leiva-Gea, I.; Sanchez-Alcoholado, L.; Queipo-Ortuño, M.I. Connection between the Gut Microbiome, Systemic Inflammation, Gut Permeability and FOXP3 Expression in Patients with Primary Sjögren’s Syndrome. Int. J. Mol. Sci. 2020, 21, 8733. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  39. Huang, Y.; Wang, Z.; Ma, H.; Ji, S.; Chen, Z.; Cui, Z.; Chen, J.; Tang, S. Dysbiosis and Implication of the Gut Microbiota in Diabetic Retinopathy. Front. Cell. Infect. Microbiol. 2021, 11, 646348. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  40. Schaefer, L.; Hernandez, H.; Coats, R.A.; Yu, Z.; Pflugfelder, S.C.; Britton, R.A.; de Paiva, C.S. Gut-derived butyrate suppresses ocular surface inflammation. Sci. Rep. 2022, 12, 4512, Erratum in Sci Rep. 2022, 12, 6581. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  41. Wang, Y.; Wei, J.; Zhang, W.; Doherty, M.; Zhang, Y.; Xie, H.; Li, W.; Wang, N.; Lei, G.; Zeng, C. Gut dysbiosis in rheumatic diseases: A systematic review and meta-analysis of 92 observational studies. EBioMedicine 2022, 80, 104055. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  42. Cao, Y.; Lu, H.; Xu, W.; Zhong, M. Gut microbiota and Sjögren’s syndrome: A two-sample Mendelian randomization study. Front. Immunol. 2023, 14, 1187906. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  43. Waters, J.L.; Ley, R.E. The human gut bacteria Christensenellaceae are widespread, heritable, and associated with health. BMC Biol. 2019, 17, 83. [Google Scholar] [CrossRef]
  44. Atarashi, K.; Tanoue, T.; Oshima, K.; Suda, W.; Nagano, Y.; Nishikawa, H.; Fukuda, S.; Saito, T.; Narushima, S.; Hase, K.; et al. Treg induction by a rationally selected mixture of Clostridia strains from the human microbiota. Nature 2013, 500, 232–236. [Google Scholar] [CrossRef]
  45. Zhang, Y.; Zhou, X.; Lu, Y. Gut microbiota and derived metabolomic profiling in glaucoma with progressive neurodegeneration. Front. Cell. Infect. Microbiol. 2022, 12, 968992. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  46. Jia, X.-M.; Wu, B.-X.; Chen, B.-D.; Li, K.-T.; Liu, Y.-D.; Xu, Y.; Wang, J.; Zhang, X. Compositional and functional aberrance of the gut microbiota in treatment-naïve patients with primary Sjögren’s syndrome. J. Autoimmun. 2023, 141, 103050. [Google Scholar] [CrossRef] [PubMed]
  47. Labetoulle, M.; Baudouin, C.; del Castillo, J.M.B.; Rolando, M.; Rescigno, M.; Messmer, E.M.; Aragona, P. How gut microbiota may impact ocular surface homeostasis and related disorders. Prog. Retin. Eye Res. 2024, 100, 101250. [Google Scholar] [CrossRef] [PubMed]
  48. Wang, X.; Pang, K.; Wang, J.; Zhang, B.; Liu, Z.; Lu, S.; Xu, X.; Zhu, L.; Zhou, Z.; Niu, M.; et al. Microbiota dysbiosis in primary Sjögren’s syndrome and the ameliorative effect of hydroxychloroquine. Cell Rep. 2022, 40, 111352. [Google Scholar] [CrossRef] [PubMed]
  49. Zhang, L.; Chu, C.-Q. Gut Microbiota-Medication Interaction in Rheumatic Diseases. Front. Immunol. 2021, 12, 796865. [Google Scholar] [CrossRef]
  50. Shi, N.; Zhang, S.; Silverman, G.; Li, M.; Cai, J.; Niu, H. Protective effect of hydroxychloroquine on rheumatoid arthritis-associated atherosclerosis. Anim. Model Exp Med. 2019, 2, 98–106. [Google Scholar] [CrossRef]
Microorganisms 13 02730 g001
Figure 1. Species richness (Chao1), Shannon index, and beta diversity. (A) Chao1 index showed significant differences only between the SJS controls and the SJS group (p = 0.012), but not between the SjD controls and the SjD group (p = 0.181). (B) Shannon diversity index showed no statistical differences in either group (p = 0.175 and p = 0.377). (C) Chao1 index showed a progressive depletion with increased ocular severity in SJS patients. (D) Principal coordinate analysis (PCoA) based on weighted UniFrac distances revealed no significant differences in beta diversity between SJS CON and the SJS group (p = 0.192) or (E) between the SjD CON and SjD group (p = 0.757). SJS, Stevens–Johnson syndrome; SjD, Sjögren’s disease; CON, healthy control group.
Figure 1. Species richness (Chao1), Shannon index, and beta diversity. (A) Chao1 index showed significant differences only between the SJS controls and the SJS group (p = 0.012), but not between the SjD controls and the SjD group (p = 0.181). (B) Shannon diversity index showed no statistical differences in either group (p = 0.175 and p = 0.377). (C) Chao1 index showed a progressive depletion with increased ocular severity in SJS patients. (D) Principal coordinate analysis (PCoA) based on weighted UniFrac distances revealed no significant differences in beta diversity between SJS CON and the SJS group (p = 0.192) or (E) between the SjD CON and SjD group (p = 0.757). SJS, Stevens–Johnson syndrome; SjD, Sjögren’s disease; CON, healthy control group.
Microorganisms 13 02730 g001
Microorganisms 13 02730 g002
Figure 2. Phylum and genus abundance. (A) No statistically significant differences were observed at the phylum level in either group. (B,C) At the genus level, Faecalibacterium was significantly less abundant in the SJS group compared to the SJS CON (p = 0.048). Prevotella showed higher relative abundance in the SJS group, although without statistical significance. SJS, Stevens–Johnson syndrome; SjD, Sjögren’s disease; CON, healthy control group.
Figure 2. Phylum and genus abundance. (A) No statistically significant differences were observed at the phylum level in either group. (B,C) At the genus level, Faecalibacterium was significantly less abundant in the SJS group compared to the SJS CON (p = 0.048). Prevotella showed higher relative abundance in the SJS group, although without statistical significance. SJS, Stevens–Johnson syndrome; SjD, Sjögren’s disease; CON, healthy control group.
Microorganisms 13 02730 g002
Microorganisms 13 02730 g003
Figure 3. Spearman’s correlation without adjustment between dry eye indices and intestinal microbiome. (A,B) In SJS, the phyla Actinobacteriota and Synergistota revealed significant negative correlations with NEI Score (r = −0.695, p = 0.038 and r = −0.722, p = 0.028, respectively). (C) The Christensenellaceae revealed a significant negative correlation with DED DEWS (r = −0.696, p = 0.037), as well as Subdoligranulum abundance (D) and the NEI Score (r = −0.690, p = 0.039). (E) A positive correlation was also observed between Clostridia abundance and TBUT (r = 0.803, p = 0.009). (F) In the SjD group, a moderate negative correlation was found between Alistipes abundance and the DED DEWS (r = −0.684, p = 0.029). NEI score, cornea fluorescein, and conjunctival lissamine green dye staining; DEWS, dry eye disease Dry Eye Workshop score; TBUT, tear break-up time.
Figure 3. Spearman’s correlation without adjustment between dry eye indices and intestinal microbiome. (A,B) In SJS, the phyla Actinobacteriota and Synergistota revealed significant negative correlations with NEI Score (r = −0.695, p = 0.038 and r = −0.722, p = 0.028, respectively). (C) The Christensenellaceae revealed a significant negative correlation with DED DEWS (r = −0.696, p = 0.037), as well as Subdoligranulum abundance (D) and the NEI Score (r = −0.690, p = 0.039). (E) A positive correlation was also observed between Clostridia abundance and TBUT (r = 0.803, p = 0.009). (F) In the SjD group, a moderate negative correlation was found between Alistipes abundance and the DED DEWS (r = −0.684, p = 0.029). NEI score, cornea fluorescein, and conjunctival lissamine green dye staining; DEWS, dry eye disease Dry Eye Workshop score; TBUT, tear break-up time.
Microorganisms 13 02730 g003
Table 1. Classification criteria for SJS severity, simplified from Sotozono et al. [24].
Table 1. Classification criteria for SJS severity, simplified from Sotozono et al. [24].
Ocular FeatureMildModerateSevere
Eyelash abnormalitiesTrichiasis or distichiasis involving less than half of the eyelidTrichiasis or distichiasis involving more than half of the eyelidTrichiasis or distichiasis involving the entire eyelid
Lid margin keratinizationContinuous keratinization up to one-third of the eyelidContinuous keratinization from one-third to one-half of the eyelidContinuous keratinization involving more than half of the eyelid
Conjunctival inflammationConjunctival hyperemia up to ++Conjunctival hyperemia +++Conjunctival hyperemia ++++
Conjunctival fibrosisConjunctival scarring up to the fornix shorteningSymblepharon without restriction of mobilitySymblepharon with restricted ocular motility
Limbal stem cell deficiencyUp to 180°Between 180° and 270°More than 270°
Corneal epitheliopathyPunctate keratopathy and epithelial erosionsEpithelial defectEpithelial defect with stromal involvement
Corneal opacityMild hazeModerate hazeDiffuse haze obscuring anterior chamber details
The + sign indicates the clinical intensity of ocular surface alterations. Grading scale: ++ moderate, +++ marked, and ++++ severe alterations.
Table 2. Demographic characteristics of study groups.
Table 2. Demographic characteristics of study groups.
N, SubjectsAge, Mean, YearsAge, Range, YearsFemale/Male
SJS controls104018–548/2
SJS93724–656/3
p value 0.517 a 0.628 b
SjD controls104939–5710/0
SjD105044–6010/0
p value 0.492 a 1 b
Comparison sets: SJS (n = 9; one exclusion due to pregnancy) matched to 10 healthy controls, and SjD (n = 10) matched to 10 healthy controls. p values were calculated using Student’s t-test (a) and Fisher’s exact test (b). SJS, Stevens–Johnson syndrome; SjD, Sjögren’s disease.
Table 3. Summary of clinical data, showing mean ± standard deviation.
Table 3. Summary of clinical data, showing mean ± standard deviation.
OSDISchirmer I TestTear Break-Up TimeNEIDED DEWS Score
ScoremmSecondsScore(Number of Patients)
SJS controls1.03 ± 2.0232.20 ± 10.1613.06 ± 2.340.25 ± 0.420 (10/10)
SJS48.26 ± 25.7112.72 ± 13.142.98 ± 1.206.83 ± 5.603 (4/9)
4 (5/9)
p valuep < 0.001 ap = 0.002 ap < 0.001 ap = 0.008 ap < 0.001 b
SjD controls1.23± 2.0032.00 ± 10.0112.68 ± 2.260.25 ± 0.420 (10/10)
SjD41.13 ± 23.8910.15 ± 11.786.27 ± 3.284.20 ± 3.193 (4/10)
4 (6/10)
p valuep < 0.001 ap < 0.001 ap < 0.001 ap = 0.003 ap < 0.001 b
p values were calculated using Student’s t-test (a) and Fisher’s exact test (b). SJS, Stevens–Johnson syndrome; SjD, Sjögren’s disease; OSDI, Ocular Surface Disease Index questionnaire; NEI score, corneal fluorescein and conjunctival lissamine green dye staining; DED DEWS, dry eye disease Dry Eye Workshop score.
Table 4. Phylum and genus abundance in SJS and the respective healthy control group.
Table 4. Phylum and genus abundance in SJS and the respective healthy control group.
Control (N = 10)SJS (N = 9)p Value
Phylum—Abundance (%), Mean ± SD (Min–Max)
Actinobacteriota1.74 ± 1.59 (0.50 to 4.70)1.09 ± 0.81 (0.24 to 2.96)0.288
Bacteroidota31.71 ± 9.85 (20.03 to 54.45)39.21 ± 13.79 (18.11 to 63.09)0.191
Campylobacterota0.00 ± 0.01 (0.00 to 0.02)0.00 ± 0.00 (0.00 to 0.00)0.343
Cyanobacteriota0.29 ± 0.35 (0.00 to 0.84)0.06 ± 0.13 (0.00 to 0.38)0.120
Desulfobacterota0.68 ± 0.70 (0.00 to 2.46)0.36 ± 0.43 (0.00 to 1.27)0.305
Elusimicrobiota0.04 ± 0.13 (0.00 to 0.41)0.00 ± 0.00 (0.00 to 0.00)0.343
Euryarchaeota0.10 ± 0.16 (0.00 to 0.47)0.05 ± 0.08 (0.00 to 0.18)0.546
Bacillota61.57 ± 10.51 (37.34 to 76.27)54.59 ± 13.45 (34.70 to 75.33)0.191
Fusobacteriota0.00 ± 0.00 (0.00 to 0.00)0.05 ± 0.14 (0.00 to 0.41)0.126
Proteobacteria3.14 ± 1.45 (0.93 to 4.97)4.14 ± 2.34 (1.59 to 8.54)0.462
Synergistota0.04 ± 0.10 (0.00 to 0.31)0.01 ± 0.02 (0.00 to 0.05)0.636
Thermoplasmatota0.06 ± 0.11 (0.00 to 0.26)0.01 ± 0.02 (0.00 to 0.06)0.252
Verrucomicrobiota0.63 ± 0.68 (0.00 to 1.97)0.42 ± 0.63 (0.00 to 1.89)0.458
Genus—Abundance (%), Mean ± SD (Min–Max)
Faecalibacterium10.38 ± 5.08 (1.17 to 16.63)5.81 ± 4.62 (0.00 to 12.65)0.048
Prevotella_91.08 ± 3.20 (0.00 to 10.19)4.63 ± 9.52 (0.00 to 27.49)0.275
Alistipes2.85 ± 2.27 (0.00 to 7.65)3.35 ± 2.91 (0.11 to 7.94)0.806
Bacteroides19.50 ± 13.46 (8.02 to 53.07)23.14 ± 15.53 (1.16 to 51.24)0.514
Blautia2.41 ± 1.81 (0.65 to 6.95)2.73 ± 1.96 (0.00 to 5.59)0.683
Christensenellaceae1.36 ± 1.63 (0.00 to 4.86)1.83 ± 2.93 (0.00 to 8.56)0.870
Clostridia_UCG-0141.12 ± 1.14 (0.00 to 3.04)0.90 ± 1.27 (0.00 to 3.49)0.836
Lachnospira1.34 ± 1.34 (0.00 to 3.88)1.21 ± 1.35 (0.00 to 3.64)0.901
Parabacteroides1.97 ± 1.26 (0.67 to 4.57)2.24 ± 1.42 (0.84 to 4.86)0.462
Ruminococcus1.03 ± 1.21 (0.00 to 2.68)1.01 ± 1.07 (0.00 to 2.90)0.933
Subdoligranulum1.13 ± 0.95 (0.00 to 3.13)0.91 ± 1.24 (0.00 to 3.14)0.410
UCG-0021.94 ± 2.00 (0.00 to 5.95)1.64 ± 1.35 (0.00 to 3.26)0.967
The table presents the mean value comparison between SJS and the respective healthy control group. Statistical significance was evaluated using the Mann–Whitney test. At the genus level, Faecalibacterium was significantly less abundant in the SJS group compared to the SJS CON (p = 0.048). This finding was further supported by the effect size estimate (Hedges’ g = 0.897), indicating a strong association between SJS and reduced Faecalibacterium abundance. SJS, Stevens–Johnson syndrome.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Frizon, L.; Rocchetti, T.T.; Frizon, A.; de Alcântara, R.J.A.; de Paiva, C.S.; Gomes, J.Á.P. The Gut Microbiome in Stevens–Johnson Syndrome and Sjögren’s Disease: Correlations with Dry Eye. Microorganisms 2025, 13, 2730. https://doi.org/10.3390/microorganisms13122730

AMA Style

Frizon L, Rocchetti TT, Frizon A, de Alcântara RJA, de Paiva CS, Gomes JÁP. The Gut Microbiome in Stevens–Johnson Syndrome and Sjögren’s Disease: Correlations with Dry Eye. Microorganisms. 2025; 13(12):2730. https://doi.org/10.3390/microorganisms13122730

Chicago/Turabian Style

Frizon, Luciana, Talita Trevizani Rocchetti, André Frizon, Rafael Jorge Alves de Alcântara, Cintia S. de Paiva, and José Álvaro Pereira Gomes. 2025. "The Gut Microbiome in Stevens–Johnson Syndrome and Sjögren’s Disease: Correlations with Dry Eye" Microorganisms 13, no. 12: 2730. https://doi.org/10.3390/microorganisms13122730

APA Style

Frizon, L., Rocchetti, T. T., Frizon, A., de Alcântara, R. J. A., de Paiva, C. S., & Gomes, J. Á. P. (2025). The Gut Microbiome in Stevens–Johnson Syndrome and Sjögren’s Disease: Correlations with Dry Eye. Microorganisms, 13(12), 2730. https://doi.org/10.3390/microorganisms13122730

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop