Analysis of the gut and oral microbiome in patients with Stevens-Johnson syndrome and Sjögren’s disease: associations with dry eye severity

Analysis of the gut and oral microbiome in patients with Stevens-Johnson syndrome and Sjögren’s disease: associations with dry eye severity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Analysis of the gut and oral microbiome in patients with Stevens-Johnson syndrome and Sjögren’s disease: associations with dry eye severity Luciana Frizon, Talita Trevizani Rochetti, André Frizon, Rafael Jorge Alves Alcântara, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6516559/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract To investigate and compare the gut and oral microbiome in patients with Stevens-Johnson syndrome (SJS), Sjögren's disease (SjD), and healthy controls, using next-generation sequencing (NGS) and correlate with dry eye parameters. Fecal samples from ten SJS, ten SjD, and ten healthy controls were analyzed. Oral swabs were obtained from six SJS, three SjD, and three healthy controls. Dry eye parameters were employed to evaluate the dry eye disease (DED). Microbiome profiles were determined by next-generation sequencing of the 16S V3-V4 region and analyzed using the Silva database. The gut microbiome showed significant differences in the SJS group, including a reduced Chao 1 index (p = 0.01) progressively correlated with increased ocular severity and decreased Faecalibacterium (p = 0.05) compared to the respective healthy control group. Severe SJS cases showed elevated Prevotella in both microbiomes. Strong correlations were observed in SJS between Christensenellaceae and DEWS score (p = 0.04), Subdoligranulum and NEI score (p = 0.04), and Clostridia with TBUT (p = 0.009). In SjD, gut profiles resembled healthy controls. The oral microbiome was similar across groups, except for higher Prevotella and Veillonella levels in SJS and SjD patients. SJS patients exhibited gut dysbiosis, characterized by reduced microbial richness and a significant decrease in the abundance of Faecalibacterium. Certain bacterial genera were correlated with the severity of dry eye, suggesting a potential link between gut microbiome alterations and the clinical progression of the disease. Biological sciences/Immunology Biological sciences/Microbiology Stevens-Johnson syndrome Sjögren's disease gut microbiome oral microbiome dry eye Figures Figure 1 Figure 2 Figure 3 INTRODUCTION The human microbiome, composed of trillions of microorganisms distributed across various body sites, is essential for development, immune regulation, and physiological balance. The gut hosts the most abundant and diverse microbial population, 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 – 5 Commensal bacteria like Faecalibacterium contribute to anti-inflammatory effects, while others, such as Prevotella copri , may activate Toll-like receptors (TLR2 and TLR4), promoting inflammation. 6 – 8 The oral microbiome also contributes to systemic and ocular health. It helps to regulate the immune balance and serves as a barrier against pathogens entering the respiratory and digestive tracts. 9 Dysbiosis in the oral cavity has been linked to SjD, with bacteria like Prevotella and Veillonella associated with heightened inflammation and severe dry eye symptoms. 10 Additionally, in severe Stevens-Johnson syndrome (SJS), oral mucosa and salivary glands are often used for surgical grafts, making the oral microbiota’s health relevant to therapeutic outcomes. 11 , 12 SJS and SjD are systemic autoimmune diseases with severe ocular manifestations, including persistent dry eye that is 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. 13 , 14 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. 14 , 15 SjD, in contrast, primarily affects exocrine glands, causing 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. 16 – 18 Both diseases share clinical features such as severe dry eye, chronic conjunctival inflammation, and reduced salivary flow. 13 , 16 DED, common in both conditions, is characterized by tear film instability, hyperosmolarity, inflammation, and neurosensory abnormalities, creating a self-perpetuating inflammatory cycle. 19 , 20 Recent studies suggest that gut microbiota modulate T cell responses and contribute to ocular surface homeostasis. Animal and human models have demonstrated that alterations in the gut microbiota can exacerbate dry eye severity via immune dysregulation. 21 – 23 While gut and oral microbiome alterations have been explored in SjD 21 , 24 , 25 and ocular surface dysbiosis has been reported in SJS, 26 – 28 no study has examined the oral and intestinal microbiome in SJS to date. This is the first study to characterize both gut and oral microbiomes in SJS patients and compare them with those of individuals with SjD and healthy controls while also evaluating correlations with dry eye severity. We hypothesize that SJS is associated with intestinal dysbiosis, which may contribute to ocular surface inflammation and the clinical severity of dry eye disease. METHODS Study design and patient selection This prospective case-control study was approved by the Ethics Committee of the Federal University of São Paulo (approval number: 6.003.698) and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to participation. A total of 32 participants were recruited from the Corneal and External Diseases Clinic at the Federal University of São Paulo between March 2023 and February 2024. For gut microbiome analysis, ten patients with chronic SJS, ten with primary SjD, and ten healthy controls were included. For oral microbiome assessment, the sample comprised six SJS patients, three with SjD, and three healthy individuals. 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 oral or gastrointestinal disorders. Exclusion criteria included trauma, infection, or surgery in the last three months, as well as antibiotic, probiotic, or prebiotic 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. Patients were stratified into mild, moderate, and severe subgroups based on ocular sequelae. One patient from this group was excluded from microbiome analyses after discovering a pregnancy post-collection. The SjD group consisted of patients with confirmed primary SjD, diagnosed according to the American College of Rheumatology/European League Against Rheumatism criteria, 29 with no associated autoimmune comorbidities. All SjD patients reported systemic involvement, dry eye and mouth symptoms, and were under oral hydroxychloroquine treatment. Healthy control subjects had no eye or oral 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. All SJS and SjD patients had a Dry eye disease score of Dry Eye Workshop (DED DEWS score) ≥ 3, and healthy control groups had a score = 0. In the gut microbiome analysis, the age and sex profiles of SJS and SjD patient groups differed. Consequently, a specific healthy control group was selected for each disease group. Ten healthy control subjects were matched by sex and age to the SJS intestinal group, and another set of ten controls was matched to the SjD group. All comparisons were performed between each disease group and its corresponding control group. For the oral microbiome, since baseline demographics were similar, the same healthy control group was used for both diseases. Clinical assessment All participants underwent a comprehensive ophthalmologic evaluation performed by the same examiner (LF). Ocular disease severity was scored following the guidelines published by the International Dry Eye Workshop. 30 The examination followed a standardized sequence: completion of the Ocular Surface Disease Index (OSDI) questionnaire, Schirmer I test, TBUT, and corneal fluorescein and conjunctival lissamine green staining, with grading according to the NEI score. The Schirmer I test evaluated basal and reflex tear secretion over five minutes. 31 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 regions, with scores from 0 to 18. The NEI score was the sum of both corneal and conjunctival staining. 32 Dry eye severity was classified using the DED DEWS system 19 ranging from 0 to 4, and incorporated results from OSDI, TBUT, Schirmer I, NEI score, and patient symptoms. Thus, more severe dry eye is associated with higher DED DEWS score, higher OSDI and NEI scores, along with lower Schirmer I test and TBUT values. 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 hours prior to the clinical visit, store it at ambient temperature, and return it on the day of the examination. For oral microbiome sampling, participants were advised to avoid eating or using oral antiseptics for at least 2 hours before collection. On the clinical examination day, patients underwent an ocular assessment, and oral microbiome samples were collected using sterile swabs from the buccal mucosa on both sides. All examinations and oral sample collections were performed by the same examiner (LF). All samples were stored at ambient temperature and shipped to the laboratory within two days for sequencing analysis. Oral and fecal microbiome analysis DNA extraction was performed using the ZymoBIOMICS DNA Kit, followed by amplification through polymerase chain reaction (PCR) targeting the V3-V4 variable region of the 16S rRNA gene. 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). Raw sequencing reads were processed using QIIME2 (version 2024.5). 33 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. 34 Statistical analysis Statistical analysis was conducted using SPSS (version 20.0, SPSS Inc., Chicago, IL) for general statistical tests and RStudio (version 4.3.1) for microbiome-specific analyses. Mean comparisons between the two groups were evaluated using the 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. 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 α-diversity, Chao1 and Shannon indices were calculated, and statistical significance was assessed using the Mann-Whitney test with Dunn’s adjustment for multiple comparisons. β-diversity was calculated using both unweighted and weighted UniFrac distances, and visualized through Principal Coordinates Analysis (PCoA) plots. Statistical significance for β-diversity was determined using permutational multivariate analysis of variance (PERMANOVA) with the adonis and pairwise functions. RESULTS Intestinal microbiome measures Age and sex distribution showed no significant differences between the SJS group and respective healthy control group and between the SjD group and respective healthy control group (Table I). The clinical examination and dry eye parameters of the SJS group and respective healthy control group and SjD group and respective healthy control group are described in Table II. Table I. Demographic characteristics of study groups in the gut microbiome. N, subjects Age, mean, years Age, range, years Female/males SJS controls 10 40 18–54 8/2 SJS 9 37 24–65 6/3 P value 0.517 a 0.628 b 10 49 39–57 10/0 SjD controls SjD 10 50 44–60 10/0 P value 0.492 a 1 b P values were calculated using the Studen’t t-test (a) and Fisher test (b) . SJS, Stevens-Johnson syndrome; SjD, Sjögren’s disease. Table II. Summary of clinical data, showing mean ± standard deviation in gut microbiome. OSDI, Schirmer I test, Tear-break-up time NEI, DED DEWS, score score mm seconds score (number of patients) SJS controls 1.03 ± 2.02 32.20 ± 10.16 13.06 ± 2.34 0.25 ± 0.42 0 (10/10) SJS 48.26 ± 25.71 12.72 ± 13.14 2.98 ± 1.20 6.83 ± 5.60 3 (4/9) 4 (5/9) P value P < 0.001 a P = 0.002 a P < 0.001 a P = 0.008 a P < 0.001 b SjD controls 1.23 ± 2.00 32.00 ± 10.01 12.68 ± 2.26 0.25 ± 0.42 0 (10/10) SjD 41.13 ± 23.89 10.15 ± 11.78 6.27 ± 3.28 4.20 ± 3.19 2 (2/10) 3 (3/10) 4 (5/10) P value P < 0.001 a P < 0.001 a P < 0.001 a P = 0.003 a P < 0.001 b P values were calculated using the Student’s t-test (a) and Fisher test (b) . SJS, Stevens-Johnson syndrome; SjD, Sjögren’s disease. With respect to 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). In contrast, the Shannon diversity index, did not show a significant difference between groups (p = 0.175). On the other hand, alpha diversity comparisons between the SjD group and respective healthy control group indicated numerical differences in Chao 1 and Shannon diversity index, however, not statistically significant (p = 0.181 and p = 0.377, respectively). 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) and between the SjD group and its respective healthy control group (p = 0.757) (Fig. 1 ). Among phyla, no statistically significant differences were observed between SJS and SjD when compared to their respective healthy control groups. Regarding genus, Faecalibacterium was significantly less abundant in the SJS group compared to the respective healthy control group (p = 0.05). No significant differences in genus levels were found between the SjD group and the respective healthy control group (Fig. 2 , Table III). Table III. Phylum and genus in SJS and control group in gut microbiome. Control (N = 10) SJS (N = 9) p Phylum - Abundance (%), Mean ± SD (Min-Max) Actinobacteriota 1.74 ± 1.59 (0.50 to 4.70) 1.09 ± 0.81 (0.24 to 2.96) 0.291 b Bacteroidota 31.71 ± 9.85 (20.03 to 54.45) 39.21 ± 13.79 (18.11 to 63.09) 0.187 b Campylobacterota 0.00 ± 0.01 (0.00 to 0.02) 0.00 ± 0.00 (0.00 to 0.00) 0.343 c Cyanobacteria 0.29 ± 0.35 (0.00 to 0.84) 0.06 ± 0.13 (0.00 to 0.38) 0.075 b Desulfobacterota 0.68 ± 0.70 (0.00 to 2.46) 0.36 ± 0.43 (0.00 to 1.27) 0.257 b Elusimicrobiota 0.04 ± 0.13 (0.00 to 0.41) 0.00 ± 0.00 (0.00 to 0.00) 0.343 c Euryarchaeota 0.10 ± 0.16 (0.00 to 0.47) 0.05 ± 0.08 (0.00 to 0.18) 0.421 b Firmicutes 61.57 ± 10.51 (37.34 to 76.27) 54.59 ± 13.45 (34.70 to 75.33) 0.222 b Fusobacteriota 0.00 ± 0.00 (0.00 to 0.00) 0.05 ± 0.14 (0.00 to 0.41) 0.126 c Proteobacteria 3.14 ± 1.45 (0.93 to 4.97) 4.14 ± 2.34 (1.59 to 8.54) 0.272 b Synergistota 0.04 ± 0.10 (0.00 to 0.31) 0.01 ± 0.02 (0.00 to 0.05) 0.636 c Thermoplasmatota 0.06 ± 0.11 (0.00 to 0.26) 0.01 ± 0.02 (0.00 to 0.06) 0.252 c Verrucomicrobiota 0.63 ± 0.68 (0.00 to 1.97) 0.42 ± 0.63 (0.00 to 1.89) 0.505 b Genus - Abundance (%), Mean ± SD (Min-Max) Faecalibacterium 10.38 ± 5.08 (1.17 to 16.63) 5.81 ± 4.62 (0.00 to 12.65) 0.050 a Prevotella_9 1.08 ± 3.20 (0.00 to 10.19) 4.63 ± 9.52 (0.00 to 27.49) 0.275 b Alistipes 2.85 ± 2.27 (0.00 to 7.65) 3.35 ± 2.91 (0.11 to 7.94) 0.684 a Bacteroides 19.50 ± 13.46 (8.02 to 53.07) 23.14 ± 15.53 (1.16 to 51.24) 0.590 a Blautia 2.41 ± 1.81 (0.65 to 6.95) 2.73 ± 1.96 (0.00 to 5.59) 0.723 a Christensenellaceae 1.36 ± 1.63 (0.00 to 4.86) 1.83 ± 2.93 (0.00 to 8.56) 0.669 a Clostridia_UCG-014 1.12 ± 1.14 (0.00 to 3.04) 0.90 ± 1.27 (0.00 to 3.49) 0.690 a Lachnospira 1.34 ± 1.34 (0.00 to 3.88) 1.21 ± 1.35 (0.00 to 3.64) 0.829 a Parabacteroides 1.97 ± 1.26 (0.67 to 4.57) 2.24 ± 1.42 (0.84 to 4.86) 0.661 a Ruminococcus 1.03 ± 1.21 (0.00 to 2.68) 1.01 ± 1.07 (0.00 to 2.90) 0.981 a Subdoligranulum 1.13 ± 0.95 (0.00 to 3.13) 0.91 ± 1.24 (0.00 to 3.14) 0.673 a UCG-002 1.94 ± 2.00 (0.00 to 5.95) 1.64 ± 1.35 (0.00 to 3.26) 0.708 a P values were calculated using the Fisher test (a) . Student (b) and Mann-Whitney (c) . Comparing Phyla data with dry eye indices in the SJS group, Spearman’s correlation analysis revealed significant negative correlations between the NEI Score and Actinobacteria (r = -0.695, p = 0.038,) as well as Synergistota (r = -0.722, p = 0.028,). These findings suggest that higher abundances of Actinobacteria and Synergistota are associated with less severe dry eye signs, 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 and dry eye indices (Fig. 3 ). 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 Score (r = -0.696, p = 0.037), as well as between 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). 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 Score (r = -0.684, p = 0.029) (Fig. 3 ). 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. Oral microbiome measures Regarding demographic data and dry eye indices in oral microbiome, the SJS and SjD groups were compared to the same healthy controls, presenting no statistically significant differences in age and sex (Table IV). However, significant differences were observed between dry eye indices between the groups, similar to the gut microbiome (Table E1 in the Online Repository at www.jacionline.org ). Table IV. Demographic characteristics of study groups in the oral microbiome. N, subjects Age, mean, years Age, range, years Female/males Controls 3 44 30–57 2/1 SJS 5 43 29–65 3/2 SjD 3 56 50–60 3/0 P value 0.308 a 0.726 b P values were calculated using the Kruskal-Wallis (a) and Fisher test (b) . SJS, Stevens-Johnson syndrome; SjD, Sjögren’s disease. Regarding alpha and beta diversity, as well as phyla and genera composition, a large similarity was observed across the SJS and SjD groups when compared to the healthy control group. Veillonella showed significantly higher abundance in the SJS and SjD groups than healthy controls (p = 0.047). Similarly, Prevotella also showed higher abundance in both groups, although this difference was not statistically significant ( p = 0.184) (Table V). Table V. Alfa diversity, phylum and genus abundance in each group in oral microbiome. Controls (N = 3) SJS (N = 5) SjD (N = 3) p Alfa diversity, Mean ± SD (Min-Max) Chao1 101.67 ± 36.02 (77.0 to 143.0) 84.20 ± 17.70 (66.0 to 104.0) 73.67 ± 2.89 (72.00 to 77.0) 0.327 Shannon 2.22 ± 0.14 (2.11 to 2.38) 2.75 ± 0.73 (1.87 to 3.47) 2.55 ± 0.39 (2.10 to 2.80) 0.804 Phylum - Abundance (%), Mean ± SD (Min-Max) Actinobacteriota 3.70 ± 2.57 (0.80 to 5.70) 4.90 ± 2.41 (1.50 to 8.00) 7.07 ± 1.36 (5.50 to 7.90) 0.273 Bacteroidota 7.70 ± 5.25 (2.10 to 12.50) 14.64 ± 12.65 (4.00 to 35.4) 9.17 ± 4.68 (3.80 to 12.40) 0.654 Campilobacterota 0.23 ± 0.12 (0.10 to 0.30) 1.00 ± 1.04 (0.30 to 2.80) 0.43 ± 0.15 (0.30 to 0.60) 0.134 Firmicutes 60.60 ± 8.55 (52.20 to 69.30) 57.3 ± 16.27 (35.20 to 74.6) 73.03 ± 1.33 (71.5to 73.8) 0.256 Fusobacteriota 3.43 ± 0.64 (2.70 to 3.90) 4.22 ± 2.53 (1.60 to 6.80) 4.67 ± 2.12 (3.20 to 7.10) 0.827 Patescibacteria 0.40 ± 0.30 (0.10 to 0.70) 1.20 ± 1.37 (0.20 to 3.50) 0.27 ± 0.15 (0.10 to 0.40) 0.277 Proteobacteria 23.83 ± 10.86 (14.40 to 35.70) 16.50 ± 3.69 (12.80 to 22.3) 5.30 ± 7.01 (1.20 to 13.40) 0.056 Spirochaetota 0.10 ± 0.10 (0.00 to 0.20) 0.22 ± 0.33 (0.00 to 0.80) 0.03 ± 0.06 (0.00 to 0.10) 0.554 Genus - Abundance (%), Mean ± SD (Min-Max) Veillonella 1.97 ± 0.60 (1.27 to 2.36) 5.54 ± 3.62 (2.61 to 11.26) 5.46 ± 2.62 (2.90 to 8.14) 0.047 Prevotella 2.23 ± 1.41 (0.66 to 3.38) 9.86 ± 11.34 (3.14 to 29.81) 7.64 ± 5.09 (1.82 to 11.32) 0.184 Actinobacillus 0.52 ± 0.47 (0.00 to 0.90) 2.03 ± 2.11 (0.00 to 4.84) 0.00 ± 0.00 (0.00 to 0.00) 0.115 Actinomyces 0.96 ± 0.49 (0.40 to 1.33) 1.96 ± 0.94 (0.55 to 3.19) 1.99 ± 0.49 (1.47 to 2.46) 0.124 Aggregatibacter 1.84 ± 1.61 (0.24 to 3.46) 0.86 ± 1.18 (0.00 to 2.91) 0.48 ± 0.82 (0.00 to 1.43) 0.267 Alloprevotella 0.70 ± 0.69 (0.00 to 1.38) 2.53 ± 2.73 (0.12 to 6.13) 0.26 ± 0.23 (0.00 to 0.42) 0.39 Fusobacterium 2.01 ± 0.08 (1.92 to 2.08) 2.90 ± 1.88 (1.43 to 5.98) 3.18 ± 3.42 (0.86 to 7.10) 0.908 Gemella 1.52 ± 0.61 (1.00 to 2.18) 1.49 ± 1.29 (0.00 to 3.55) 5.02 ± 5.82 (0.00 to 11.40) 0.653 Haemophilus 16.98 ± 9.34 (9.95 to 27.57) 11.17 ± 3.56 (7.66 to 16.87) 3.92 ± 5.76 (0.16 to 10.55) 0.144 Leptotrichia 1.41 ± 0.70 (0.64 to 1.99) 1.27 ± 1.39 (0.18 to 3.50) 1.49 ± 1.41 (0.00 to 2.80) 0.908 Neisseria 2.12 ± 0.52 (1.54 to 2.57) 1.38 ± 0.75 (0.22 to 2.30) 0.24 ± 0.24 (0.00 to 0.48) 0.064 Porphyromonas 3.58 ± 2.81 (0.59 to 6.15) 1.28 ± 2.34 (0.03 to 5.46) 0.37 ± 0.22 (0.13 to 0.55) 0.124 Rothia 2.35 ± 1.83 (0.26 to 3.62) 2.05 ± 0.92 (0.90 to 3.26) 3.64 ± 1.81 (2.1 to 5.64) 0.486 Streptococcus 55.11 ± 7.10 (47.89 to 62.08) 44.6 ± 22.25 (13.67 to 67.19) 58.27 ± 10.22 (49.4 to 69.4) 0.545 P values were calculated using the Kruskal-Wallis test. SJS, Stevens-Johnson syndrome; SjD, Sjögren’s disease. Two patients with severe ocular SJS (representing 100% of the severe group analyzed for both microbiomes) exhibited a markedly elevated abundance of Prevotella in both the gut and oral microbiomes; however, this difference was not statistically significant. One patient showed Prevotella accounting for 27.59% of the gut microbiome and 29.81% of the oral microbiome, while the other demonstrated Prevotella comprising 12.63% of the gut microbiome and 8.41% of the oral microbiome. Taken together, our findings indicate the presence of gut dysbiosis in SJS patients, characterized by a significantly reduced Chao1 index (p = 0.012), with a progressive decline correlating with increased ocular surface severity. A reduction in Faecalibacterium abundance was also observed (p = 0.050). Additionally, correlations were identified between certain bacterial phyla and genera, including Christensenellaceae, Subdoligranulum, and Clostridia , and higher dry eye indices. Severe SJS cases showed elevated Prevotella in both microbiomes. DISCUSSION Our study demonstrated that patients with SJS exhibit significant gut dysbiosis, characterized by reduced alpha diversity, as indicated by lower Chao1 indices, which progressively correlated with increased ocular surface severity. In addition, distinct alterations in gut microbial composition were observed when compared to healthy controls. While previous studies described ocular surface and lid margin dysbiosis in SJS, 27 , 28 , 35 our study is the first to simultaneously assess both gut and oral microbiomes in this patient population, while also comparing them with patients with primary SjD and healthy individuals. Furthermore, we examined correlations between gut microbial composition and dry eye parameters. Our findings support the hypothesis that gut microbial dysbiosis contributes to the pathogenesis of SJS, as has been reported in various inflammatory and autoimmune diseases. This influence may occur through multiple pathways, including the gut–eye axis, with particular microbial taxa in the gut being implicated in ocular disease. The intestinal commensal microorganism plays a critical role in limiting pathogen colonization and maintaining mucosal immune homeostasis throughout the body. 7 , 23 In this study, alpha-diversity was significantly reduced in SJS patients compared to healthy controls, and this reduction, particularly in the Chao1 index, correlated with increasing ocular inflammation and disease severity. This is consistent with findings in Low et al. 36 in mucous membrane pemphigoid, another progressive autoimmune scarring disorder involving mucosal sites, including the ocular surface, and characterized by autoreactive T cells and cicatrizing conjunctivitis, paralleling aspects of SJS. Similarly, lower alpha-diversity has been reported in uveitis, 37 Behçet’s disease, 38 Sjögren’s disease, 21 , 39 , 40 diabetic retinopathy. 41 Reduced gut microbial diversity is generally associated with higher levels of systemic inflammation and compromised intestinal barrier function, facilitating the expansion of pathogenic taxa and perpetuating immune activation. 18 , 21 A notable finding in our cohort of SJS patients was the depletion of Faecalibacterium , a key butyrate-producing genus with anti-inflammatory properties. 7 This observation agrees with the findings in uveitis, 37 SjD 21 , 25 and Behçet's disease. 38 In mucous membrane pemphigoid, Lower et al. 36 also reported reduced alpha-diversity with increased ocular severity and depletion of butyrate-producing taxa. Faecalibacterium plays a critical role in intestinal homeostasis by supporting barrier integrity, promoting tolerogenic dendritic cells and FOXP3 + regulatory T cells (Tregs), and modulating cytokine production. Short-chain fatty acids (SCFAs), particularly butyrate, exert potent immunomodulatory effects and are largely derived from microbial fermentation of dietary starches. 7 Schaefer et al. 23 demonstrated in both SjD patients and mouse models that intestinal commensals, such as Faecalibacterium , can influence ocular surface health by promoting the development of Tregs in draining lymph nodes. We also observed an increased abundance of the pro-inflammatory genus Prevotella in both gut and oral microbiome of SJS patients, particularly in severe cases. This finding is consistent with reports in uveitis, 37 diabetic retinopathy, 42 and SjD. 24 , 43 Elevated Prevotella abundance has been linked to dry eye severity. 24 , 40 , 43 Prevotella is known to degrade mucin, disrupt the colonic barrier, and stimulate immune activation via Toll-like receptors TLR2 and TLR4, contributing to chronic inflammation and autoimmune pathology, including rheumatoid arthritis and immune-mediated dry eye. 6 , 9 , 44 , 45 In line with expectations, the SJS group exhibited worse dry eye outcomes, with DED DEWS scores ranging from 3 to 4 and lower microbial diversity correlating with ocular severity. These results align with those of de Paiva et al. 21 and Moon et al. 24 , who found that patients with more severe DED in SjD also had lower gut microbial diversity. Our study further correlated microbial composition at the phylum and genus levels with specific dry eye parameters. At the phylum level, increased abundance of Actinobacteria and Synergistota was associated with lower NEI scores, indicating less ocular surface damage. Moon et al. 24 reported similar findings, linking Actinobacteria with improved TBUT and reduced dry eye severity, and Wang et al. 45 found an association between Actinobacteria and reduced dry eye severity. At the genus level, Subdoligranulum abundance was negatively correlated with NEI scores, consistent with reports by Cao et al. 46 and Moon et al. 24 , where this genus was depleted in both SjD and non-Sjögren dry eye groups. We also observed increased abundance of Christensenellaceae correlated with lower DED DEWS scores, and Clostridia was positively associated with TBUT. These taxa may play a protective role and be associated with milder forms of DED in SJS. In our cohort, alpha-diversity (Chao1 and Shannon indices) did not differ significantly between the SjD and control group, consistent with findings from Mendez et al. 25 , Moon et al. 24 , and Zhang et al. 43 , but diverging from those of Schaefer et al. 23 , de Paiva et al. 21 , and Cano-Ortiz et al. 40 Beta diversity and microbial composition at the phylum and genus levels were also largely similar to the healthy control group, with some variations that did not reach statistical significance. These discrepancies may stem from the small sample size, population differences (e.g., geography, diet, sex, age), and disease severity heterogeneity. Regarding the oral microbiome, notable differences included elevated levels of Veillonella and Prevotella in both SJS and SjD patients compared to the control group. These genera have been linked to poor oral health and periodontal disease. 47 Our findings are in line with previous studies reporting minimal differences in oral microbial diversity between DED, SjD, and controls. 48 , 49 Zhou et al. 48 found increased Bacteroidete s and Firmicutes with reduced Proteobacteri a in SjD oral samples. Despite its strengths, this study has several limitations. It is a cross-sectional, observational study with a relatively small sample size, especially for a rare disease like SJS, and lacks longitudinal follow-up. The oral microbiome subgroup was limited, with only three patients in both the healthy and SjD groups. This was included to explore potential microbiome continuity along the digestive tract. Furthermore, while 16S rRNA sequencing provides valuable genus-level insights, it lacks the resolution for species or strain-level identification. 50 More comprehensive approaches, such as shotgun metagenomics or whole-genome sequencing, could offer deeper insight into microbial functionality. Integration with host inflammatory markers and metabolomic profiling would further enhance mechanistic understanding. In summary, this study demonstrates that patients with SJS exhibit significant gut dysbiosis, with reduced microbial richness, depletion of anti-inflammatory bacteria ( Faecalibacterium ), and enrichment of pro-inflammatory taxa ( Prevotella ), particularly in severe cases. Notably, Prevotella abundance was elevated in both gut and oral microbiomes of SJS patients. These findings highlight a complex interplay between the gut and oral microbiome and inflammatory pathways underlying SJS. Declarations Data availability The datasets used and analyzed during the current study are available from the corresponding author (L.F.) upon reasonable request. Author contributions LF wrote the original draft and made subsequent revisions, TTR contributed to the methodology and writing of the original draft reviewing and editing the final manuscript, AF contributed to the writing, reviewing and editing the final manuscript, RJAA contributed to the methodology, reviewing and editing the final manuscript, CSP contributed to the analysis, reviewing and editing the final manuscript, JAPG contributed to the design of the study, reviewing and editing the final manuscript. All authors approved the final version of the manuscript. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. CSdP receives salary support from the Caroline Elles Professorship. Additional Information Competing Interests Statement Outside any subjects treated 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 Human Microbiome Project Consortium. A framework for human microbiome research. Nature 486 (7402), 215–221. 10.1038/nature11209 (2012). PMID: 22699610; PMCID: PMC3377744. Aagaard, K. et al. The Human Microbiome Project strategy for comprehensive sampling of the human microbiome and why it matters. FASEB J. 27 (3), 1012–1022. 10.1096/fj.12-220806 (2013). PMID: 23165986; PMCID: PMC3574278. Zárate-Bladés, C. R. et al. Gut microbiota as a source of a surrogate antigen that triggers autoimmunity in an immune-privileged site. Gut Microbes . 8 (1), 59–66 (2017). PMID: 28045579; PMCID: PMC5361604. Wang, C. et al. Dysbiosis Modulates Ocular Surface Inflammatory Response to Liposaccharide. Invest. Ophthalmol. Vis. Sci. 60 (13), 4224–4233. 10.1167/iovs.19-27939 (2019). PMID: 31618426; PMCID: PMC6795342. Campagnoli, L. I. 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Outside any subjects treated 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. Supplementary Files TableE1Oralclinicaldata.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6516559","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":458297600,"identity":"182ddeca-a03d-4fbb-82d5-9937f3d2f729","order_by":0,"name":"Luciana Frizon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACPiA+AMT8DMwgbgUQMzM34NXCBtUi2QDWcgakhZGwFgawFhDJ2AYmCWiRSH544OcOOwnzdvaHjwvn1UbztwO1/KjYhkdLmsHB3jPJEjKHGZKNZ247njvjMGMDY8+Z23i05DAc4G1jrpNgZjgmzbvtWG4DUAszYxt+LQf/ttVLSACVSfPOOZY7nxgth3nbDgO1MLNJ8zbU5G4gqIXnmcFh2bbjQC1szMY8xw7kbgRqOYjPL/zsyY8/vm2rlpDgP/7wMU9NXe6884cPPvhRgVsLOjgMJg8QrR4I6khRPApGwSgYBSMEAACxEFIo/I8s+wAAAABJRU5ErkJggg==","orcid":"","institution":"Universidade Federal de São Paulo","correspondingAuthor":true,"prefix":"","firstName":"Luciana","middleName":"","lastName":"Frizon","suffix":""},{"id":458297601,"identity":"29070f5f-fe8f-44e8-b739-55bb59038b8b","order_by":1,"name":"Talita Trevizani Rochetti","email":"","orcid":"","institution":"Universidade Federal de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Talita","middleName":"Trevizani","lastName":"Rochetti","suffix":""},{"id":458297604,"identity":"be0aad21-e673-4d70-826a-f1bfa0489b0f","order_by":2,"name":"André Frizon","email":"","orcid":"","institution":"Universidade Federal de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"André","middleName":"","lastName":"Frizon","suffix":""},{"id":458297605,"identity":"51877369-2188-4071-b202-70f591c4d6f5","order_by":3,"name":"Rafael Jorge Alves Alcântara","email":"","orcid":"","institution":"Universidade Federal de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"Jorge Alves","lastName":"Alcântara","suffix":""},{"id":458297606,"identity":"9de753d5-c72a-4a19-8c86-5609249c05cc","order_by":4,"name":"Cintia S. 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(\u003cstrong\u003eB\u003c/strong\u003e) Shannon diversity index showed no statistical differences in either group (\u003cem\u003ep\u003c/em\u003e = 0.175 and \u003cem\u003ep\u003c/em\u003e = 0.377). (\u003cstrong\u003eC\u003c/strong\u003e) Chao1 index showed a progressive depletion with increased ocular severity in SJS patients. (\u003cstrong\u003eD\u003c/strong\u003e) Weighted UniFrac beta diversity analysis revealed no significant differences between SJS CON and the SJS group (p = 0.192) and (\u003cstrong\u003eE\u003c/strong\u003e) between the SjD CON and SjD group (\u003cem\u003ep\u003c/em\u003e = 0.757). SJS, Stevens-Johnson syndrome; SjD,Sjögren’s disease, CON, Health control group.\u003c/p\u003e","description":"","filename":"Fig1Alphaandbetadiversitygut.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6516559/v1/e4d83ea9ab76ce60a4a2a32e.jpg"},{"id":83157884,"identity":"91f893ff-9367-49bf-82c8-74fbb488c14a","added_by":"auto","created_at":"2025-05-20 14:53:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":420127,"visible":true,"origin":"","legend":"\u003cp\u003ePhylum and genus abundance in the intestinal microbiome.\u003cstrong\u003e\u003cbr\u003e\n \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) No statistically significant differences were observed at the phylum level in either group. (\u003cstrong\u003eB \u003c/strong\u003eand\u003cstrong\u003e C\u003c/strong\u003e) At the genus level, \u003cem\u003eFaecalibacterium\u003c/em\u003e was significantly less abundant in the SJS group compared to the SJS CON (\u003cem\u003ep\u003c/em\u003e = 0.05). SJS, Stevens-Johnson syndrome; SjD, Sjögren’s disease, CON, Health control group.\u003c/p\u003e","description":"","filename":"Fig2Phylumandgeneragut.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6516559/v1/893c3e3f4b6e6d7ec4956952.jpg"},{"id":83157868,"identity":"d2408620-8749-49e8-9857-03e4937dc1dd","added_by":"auto","created_at":"2025-05-20 14:53:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":182453,"visible":true,"origin":"","legend":"\u003cp\u003eSperman’s correlation between dry eye indices and intestinal microbiome. (\u003cstrong\u003eA\u003c/strong\u003e) In SJS the phyla \u003cem\u003eActinobacteria\u003c/em\u003erevealed significant negative correlations between NEI Score (\u003cem\u003er\u003c/em\u003e = -0.695, \u003cem\u003ep\u003c/em\u003e = 0.038) as well as \u003cem\u003eSynergistota\u003c/em\u003e (\u003cem\u003er\u003c/em\u003e = -0.722, \u003cem\u003ep\u003c/em\u003e= 0.028) (\u003cstrong\u003eB\u003c/strong\u003e). (\u003cstrong\u003eC\u003c/strong\u003e) The genera \u003cem\u003eChristensenellaceae \u003c/em\u003erevealed significant negative correlation with DED DEWS Score (\u003cem\u003er\u003c/em\u003e = -0.696, \u003cem\u003ep\u003c/em\u003e= 0.037), as \u003cem\u003eSubdoligranulum\u003c/em\u003e abundance (\u003cstrong\u003eD\u003c/strong\u003e) and the NEI Score (\u003cem\u003er\u003c/em\u003e= -0.690, \u003cem\u003ep\u003c/em\u003e = 0.039). (\u003cstrong\u003eE\u003c/strong\u003e) Positive correlation was also observed between \u003cem\u003eClostridia\u003c/em\u003e abundance and TBUT (\u003cem\u003er\u003c/em\u003e = 0.803, \u003cem\u003ep\u003c/em\u003e = 0.009). (\u003cstrong\u003eF\u003c/strong\u003e) In the SjD group, a moderate negative correlation was found between \u003cem\u003eAlistipes\u003c/em\u003e abundance and the DED DEWS Score (\u003cem\u003er\u003c/em\u003e = -0.684, \u003cem\u003ep\u003c/em\u003e = 0.029). NEI score, cornea fluorescein and conjunctival lissamine green dye staining; DED DEWS score, Dry Eye Disease International Dry Eye Workshop; TBUT, tear breakup time.\u003c/p\u003e","description":"","filename":"Fig3Generaanddryeyeindices.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6516559/v1/3246a4a1f64708bbfc155396.jpg"},{"id":84342776,"identity":"de2b8f60-60af-4966-bdaf-b65a5706383d","added_by":"auto","created_at":"2025-06-10 19:01:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2365234,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6516559/v1/f3c6ce37-8f6e-4deb-a8ee-df4916a3737f.pdf"},{"id":83157886,"identity":"9100bcf4-3550-4e02-bd04-507e10f37f51","added_by":"auto","created_at":"2025-05-20 14:53:46","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":16366,"visible":true,"origin":"","legend":"","description":"","filename":"TableE1Oralclinicaldata.docx","url":"https://assets-eu.researchsquare.com/files/rs-6516559/v1/bfcb068b1aa4c66135ecd977.docx"}],"financialInterests":"Competing interest reported. Outside any subjects treated 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 \u0026 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. \nAll other authors declare no conflicts of interest.","formattedTitle":"Analysis of the gut and oral microbiome in patients with Stevens-Johnson syndrome and Sjögren’s disease: associations with dry eye severity","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe human microbiome, composed of trillions of microorganisms distributed across various body sites, is essential for development, immune regulation, and physiological balance. The gut hosts the most abundant and diverse microbial population, shaped by diet, lifestyle, and environment.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Dysbiosis, marked by reduced microbial richness and diversity, can impair systemic homeostasis and has been linked to diseases affecting distant organs, including the eyes.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eThe concept of a \"gut\u0026ndash;eye axis\" has emerged, implicating gut microbial imbalances in ocular inflammatory and autoimmune disorders, such as Sj\u0026ouml;gren\u0026rsquo;s disease (SjD), dry eye disease (DED), uveitis, and diabetic retinopathy.\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Commensal bacteria like \u003cem\u003eFaecalibacterium\u003c/em\u003e contribute to anti-inflammatory effects, while others, such as \u003cem\u003ePrevotella copri\u003c/em\u003e, may activate Toll-like receptors (TLR2 and TLR4), promoting inflammation.\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe oral microbiome also contributes to systemic and ocular health. It helps to regulate the immune balance and serves as a barrier against pathogens entering the respiratory and digestive tracts.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Dysbiosis in the oral cavity has been linked to SjD, with bacteria like \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eVeillonella\u003c/em\u003e associated with heightened inflammation and severe dry eye symptoms.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Additionally, in severe Stevens-Johnson syndrome (SJS), oral mucosa and salivary glands are often used for surgical grafts, making the oral microbiota\u0026rsquo;s health relevant to therapeutic outcomes.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSJS and SjD are systemic autoimmune diseases with severe ocular manifestations, including persistent dry eye that is 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.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Genetic and immune-mediated mechanisms, particularly T cell\u0026ndash;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.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e SjD, in contrast, primarily affects exocrine glands, causing 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.\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Both diseases share clinical features such as severe dry eye, chronic conjunctival inflammation, and reduced salivary flow.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDED, common in both conditions, is characterized by tear film instability, hyperosmolarity, inflammation, and neurosensory abnormalities, creating a self-perpetuating inflammatory cycle.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Recent studies suggest that gut microbiota modulate T cell responses and contribute to ocular surface homeostasis. Animal and human models have demonstrated that alterations in the gut microbiota can exacerbate dry eye severity via immune dysregulation.\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhile gut and oral microbiome alterations have been explored in SjD \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and ocular surface dysbiosis has been reported in SJS,\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e no study has examined the oral and intestinal microbiome in SJS to date. This is the first study to characterize both gut and oral microbiomes in SJS patients and compare them with those of individuals with SjD and healthy controls while also evaluating correlations with dry eye severity. We hypothesize that SJS is associated with intestinal dysbiosis, which may contribute to ocular surface inflammation and the clinical severity of dry eye disease.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patient selection\u003c/h2\u003e \u003cp\u003e This prospective case-control study was approved by the Ethics Committee of the Federal University of S\u0026atilde;o Paulo (approval number: 6.003.698) and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to participation.\u003c/p\u003e \u003cp\u003e A total of 32 participants were recruited from the Corneal and External Diseases Clinic at the Federal University of S\u0026atilde;o Paulo between March 2023 and February 2024. For gut microbiome analysis, ten patients with chronic SJS, ten with primary SjD, and ten healthy controls were included. For oral microbiome assessment, the sample comprised six SJS patients, three with SjD, and three healthy individuals. 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 oral or gastrointestinal disorders. Exclusion criteria included trauma, infection, or surgery in the last three months, as well as antibiotic, probiotic, or prebiotic use during that period.\u003c/p\u003e \u003cp\u003eThe SJS group included patients with chronic disease following a history of mucocutaneous inflammation induced by medications or infections, involving at least two mucous membranes. Patients were stratified into mild, moderate, and severe subgroups based on ocular sequelae. One patient from this group was excluded from microbiome analyses after discovering a pregnancy post-collection. The SjD group consisted of patients with confirmed primary SjD, diagnosed according to the American College of Rheumatology/European League Against Rheumatism criteria,\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e with no associated autoimmune comorbidities. All SjD patients reported systemic involvement, dry eye and mouth symptoms, and were under oral hydroxychloroquine treatment.\u003c/p\u003e \u003cp\u003eHealthy control subjects had no eye or oral irritation, a tear break-up time (TBUT)\u0026thinsp;\u0026ge;\u0026thinsp;7 s, Schirmer I\u0026thinsp;\u0026ge;\u0026thinsp;10 mm, corneal fluorescein score\u0026thinsp;\u0026le;\u0026thinsp;2, conjunctival lissamine score\u0026thinsp;\u0026le;\u0026thinsp;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. All SJS and SjD patients had a Dry eye disease score of Dry Eye Workshop (DED DEWS score)\u0026thinsp;\u0026ge;\u0026thinsp;3, and healthy control groups had a score\u0026thinsp;=\u0026thinsp;0.\u003c/p\u003e \u003cp\u003eIn the gut microbiome analysis, the age and sex profiles of SJS and SjD patient groups differed. Consequently, a specific healthy control group was selected for each disease group. Ten healthy control subjects were matched by sex and age to the SJS intestinal group, and another set of ten controls was matched to the SjD group. All comparisons were performed between each disease group and its corresponding control group. For the oral microbiome, since baseline demographics were similar, the same healthy control group was used for both diseases.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical assessment\u003c/h3\u003e\n\u003cp\u003eAll participants underwent a comprehensive ophthalmologic evaluation performed by the same examiner (LF). Ocular disease severity was scored following the guidelines published by the International Dry Eye Workshop.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e The examination followed a standardized sequence: completion of the Ocular Surface Disease Index (OSDI) questionnaire, Schirmer I test, TBUT, and corneal fluorescein and conjunctival lissamine green staining, with grading according to the NEI score.\u003c/p\u003e \u003cp\u003eThe Schirmer I test evaluated basal and reflex tear secretion over five minutes.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e 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\u0026ndash;3 per region), with a maximum score of 15. Conjunctival staining with lissamine green was assessed over six regions, with scores from 0 to 18. The NEI score was the sum of both corneal and conjunctival staining.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDry eye severity was classified using the DED DEWS system\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e ranging from 0 to 4, and incorporated results from OSDI, TBUT, Schirmer I, NEI score, and patient symptoms. Thus, more severe dry eye is associated with higher DED DEWS score, higher OSDI and NEI scores, along with lower Schirmer I test and TBUT values.\u003c/p\u003e\n\u003ch3\u003eSample collection\u003c/h3\u003e\n\u003cp\u003e 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 hours prior to the clinical visit, store it at ambient temperature, and return it on the day of the examination. For oral microbiome sampling, participants were advised to avoid eating or using oral antiseptics for at least 2 hours before collection. On the clinical examination day, patients underwent an ocular assessment, and oral microbiome samples were collected using sterile swabs from the buccal mucosa on both sides. All examinations and oral sample collections were performed by the same examiner (LF). All samples were stored at ambient temperature and shipped to the laboratory within two days for sequencing analysis.\u003c/p\u003e\n\u003ch3\u003eOral and fecal microbiome analysis\u003c/h3\u003e\n\u003cp\u003eDNA extraction was performed using the ZymoBIOMICS DNA Kit, followed by amplification through polymerase chain reaction (PCR) targeting the V3-V4 variable region of the 16S rRNA gene. The primers 515F (5\u0026rsquo;-GTGCCAGCMGCCGCGGTAA-3\u0026rsquo;) and 806R (5\u0026rsquo;-GGACTACHVGGGTWTCTAAT-3\u0026rsquo;) were employed for amplification. Sequencing was carried out on the Illumina MiSeq platform using the MiSeq v2 kit (Illumina, San Diego, CA). Raw sequencing reads were processed using QIIME2 (version 2024.5).\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e 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.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted using SPSS (version 20.0, SPSS Inc., Chicago, IL) for general statistical tests and RStudio (version 4.3.1) for microbiome-specific analyses. Mean comparisons between the two groups were evaluated using the Student\u0026rsquo;s t-test, Fisher\u0026rsquo;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\u0026rsquo;s correlation was applied to assess linear associations between variables due to non-normal distributions. In the genus-level analysis, only genera with an average relative abundance of at least 1% were included. Statistical significance was set at p\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eMicrobiome data analysis and visualization were performed using the phyloseq, vegan, ggplot2, and microbiome packages in RStudio. The α-diversity, Chao1 and Shannon indices were calculated, and statistical significance was assessed using the Mann-Whitney test with Dunn\u0026rsquo;s adjustment for multiple comparisons. β-diversity was calculated using both unweighted and weighted UniFrac distances, and visualized through Principal Coordinates Analysis (PCoA) plots. Statistical significance for β-diversity was determined using permutational multivariate analysis of variance (PERMANOVA) with the adonis and pairwise functions.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIntestinal microbiome measures\u003c/h2\u003e \u003cp\u003eAge and sex distribution showed no significant differences between the SJS group and respective healthy control group and between the SjD group and respective healthy control group (Table I). The clinical examination and dry eye parameters of the SJS group and respective healthy control group and SjD group and respective healthy control group are described in Table II.\u003c/p\u003e \u003cp\u003eTable I. Demographic characteristics of study groups in the gut microbiome.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN, subjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, mean, years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge, range, years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFemale/males\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSJS controls\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8/2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSJS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6/3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.517\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.628\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e39\u0026ndash;57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10/0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSjD controls\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSjD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10/0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.492\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eP values were calculated using the Studen\u0026rsquo;t t-test \u003csup\u003e(a)\u003c/sup\u003e and Fisher test \u003csup\u003e(b)\u003c/sup\u003e. SJS, Stevens-Johnson syndrome; SjD, Sj\u0026ouml;gren\u0026rsquo;s disease.\u003c/p\u003e \u003cp\u003eTable II. Summary of clinical data, showing mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation in gut microbiome.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOSDI,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSchirmer I test,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTear-break-up time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNEI,\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDED DEWS, score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003escore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eseconds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003escore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(number of patients)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSJS controls\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.20\u0026thinsp;\u0026plusmn;\u0026thinsp;10.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (10/10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSJS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.26\u0026thinsp;\u0026plusmn;\u0026thinsp;25.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.72\u0026thinsp;\u0026plusmn;\u0026thinsp;13.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (4/9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (5/9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.002\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.008\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSjD controls\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (10/10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSjD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.13\u0026thinsp;\u0026plusmn;\u0026thinsp;23.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.15\u0026thinsp;\u0026plusmn;\u0026thinsp;11.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.20\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (2/10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (3/10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (5/10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.003\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eP values were calculated using the Student\u0026rsquo;s t-test \u003csup\u003e(a)\u003c/sup\u003e and Fisher test \u003csup\u003e(b)\u003c/sup\u003e. SJS, Stevens-Johnson syndrome; SjD, Sj\u0026ouml;gren\u0026rsquo;s disease.\u003c/p\u003e \u003cp\u003eWith respect to alpha diversity, the Chao1 index, which represents species richness, was significantly lower in the SJS group compared to the respective healthy control group (p\u0026thinsp;=\u0026thinsp;0.012) and demonstrated a progressive decline correlating with increased ocular surface severity (p\u0026thinsp;=\u0026thinsp;0.01). In contrast, the Shannon diversity index, did not show a significant difference between groups (p\u0026thinsp;=\u0026thinsp;0.175). On the other hand, alpha diversity comparisons between the SjD group and respective healthy control group indicated numerical differences in Chao 1 and Shannon diversity index, however, not statistically significant (p\u0026thinsp;=\u0026thinsp;0.181 and p\u0026thinsp;=\u0026thinsp;0.377, respectively). Beta diversity analysis using weighted and unweighted UniFrac distances revealed no significant differences between the SJS group and its respective healthy control group (p\u0026thinsp;=\u0026thinsp;0.192) and between the SjD group and its respective healthy control group (p\u0026thinsp;=\u0026thinsp;0.757) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong phyla, no statistically significant differences were observed between SJS and SjD when compared to their respective healthy control groups. Regarding genus, \u003cem\u003eFaecalibacterium\u003c/em\u003e was significantly less abundant in the SJS group compared to the respective healthy control group (p\u0026thinsp;=\u0026thinsp;0.05). No significant differences in genus levels were found between the SjD group and the respective healthy control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table III).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable III. Phylum and genus in SJS and control group in gut microbiome.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl (N\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSJS (N\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePhylum - Abundance (%), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Min-Max)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActinobacteriota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59 (0.50 to 4.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81 (0.24 to 2.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.291\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteroidota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.71\u0026thinsp;\u0026plusmn;\u0026thinsp;9.85 (20.03 to 54.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.21\u0026thinsp;\u0026plusmn;\u0026thinsp;13.79 (18.11 to 63.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.187\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCampylobacterota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 (0.00 to 0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 (0.00 to 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.343\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyanobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35 (0.00 to 0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13 (0.00 to 0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.075\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDesulfobacterota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70 (0.00 to 2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43 (0.00 to 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.257\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElusimicrobiota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13 (0.00 to 0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 (0.00 to 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.343\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuryarchaeota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16 (0.00 to 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 (0.00 to 0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.421\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.57\u0026thinsp;\u0026plusmn;\u0026thinsp;10.51 (37.34 to 76.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.59\u0026thinsp;\u0026plusmn;\u0026thinsp;13.45 (34.70 to 75.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.222\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFusobacteriota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 (0.00 to 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 (0.00 to 0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.126\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45 (0.93 to 4.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34 (1.59 to 8.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.272\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSynergistota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 (0.00 to 0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 (0.00 to 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.636\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThermoplasmatota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 (0.00 to 0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 (0.00 to 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.252\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVerrucomicrobiota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68 (0.00 to 1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63 (0.00 to 1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.505\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGenus - Abundance (%), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Min-Max)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFaecalibacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.08 (1.17 to 16.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.62 (0.00 to 12.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.050\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrevotella_9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20 (0.00 to 10.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;9.52 (0.00 to 27.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.275\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAlistipes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27 (0.00 to 7.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.91 (0.11 to 7.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.684\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBacteroides\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.50\u0026thinsp;\u0026plusmn;\u0026thinsp;13.46 (8.02 to 53.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.14\u0026thinsp;\u0026plusmn;\u0026thinsp;15.53 (1.16 to 51.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.590\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBlautia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81 (0.65 to 6.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 (0.00 to 5.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.723\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChristensenellaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63 (0.00 to 4.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;2.93 (0.00 to 8.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.669\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClostridia_UCG-014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14 (0.00 to 3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27 (0.00 to 3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.690\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLachnospira\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34 (0.00 to 3.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35 (0.00 to 3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.829\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eParabacteroides\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26 (0.67 to 4.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42 (0.84 to 4.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.661\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRuminococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21 (0.00 to 2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07 (0.00 to 2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.981\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSubdoligranulum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95 (0.00 to 3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24 (0.00 to 3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.673\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUCG-002\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00 (0.00 to 5.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35 (0.00 to 3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.708\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eP values were calculated using the Fisher test \u003csup\u003e(a)\u003c/sup\u003e. Student \u003csup\u003e(b)\u003c/sup\u003e and Mann-Whitney \u003csup\u003e(c)\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eComparing Phyla data with dry eye indices in the SJS group, Spearman\u0026rsquo;s correlation analysis revealed significant negative correlations between the NEI Score and Actinobacteria (r = -0.695, p\u0026thinsp;=\u0026thinsp;0.038,) as well as Synergistota (r = -0.722, p\u0026thinsp;=\u0026thinsp;0.028,). These findings suggest that higher abundances of Actinobacteria and Synergistota are associated with less severe dry eye signs, 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 and dry eye indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCorrelations between bacterial genera and dry eye indices in the SJS group, Spearman\u0026rsquo;s correlation revealed a moderate negative correlation between \u003cem\u003eChristensenellaceae\u003c/em\u003e abundance and the DED DEWS Score (r = -0.696, p\u0026thinsp;=\u0026thinsp;0.037), as well as between \u003cem\u003eSubdoligranulum\u003c/em\u003e abundance and the NEI Score (r = -0.690, p\u0026thinsp;=\u0026thinsp;0.039). A positive correlation was also observed between \u003cem\u003eClostridia\u003c/em\u003e abundance and TBUT (r\u0026thinsp;=\u0026thinsp;0.803, p\u0026thinsp;=\u0026thinsp;0.009). These results indicate that higher abundances of \u003cem\u003eChristensenellaceae\u003c/em\u003e, \u003cem\u003eSubdoligranulum\u003c/em\u003e, and \u003cem\u003eClostridia\u003c/em\u003e are associated with less severe dry eye parameters in the SJS group. In the SjD group, a moderate negative correlation was found between \u003cem\u003eAlistipes\u003c/em\u003e abundance and the DED DEWS Score (r = -0.684, p\u0026thinsp;=\u0026thinsp;0.029) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the ocular SJS group severity (mild, moderate, and severe ocular severity grades), Spearman\u0026rsquo;s correlation analysis revealed a moderate positive correlation between disease severity and the abundance of Cyanobacteria (p\u0026thinsp;=\u0026thinsp;0.050, r\u0026thinsp;=\u0026thinsp;0.659) and Fusobacteria (p\u0026thinsp;=\u0026thinsp;0.050, r\u0026thinsp;=\u0026thinsp;0.659) at the phylum level. No statistically significant correlations were observed at the genus level.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOral microbiome measures\u003c/h3\u003e\n\u003cp\u003eRegarding demographic data and dry eye indices in oral microbiome, the SJS and SjD groups were compared to the same healthy controls, presenting no statistically significant differences in age and sex (Table IV). However, significant differences were observed between dry eye indices between the groups, similar to the gut microbiome (Table E1 in the Online Repository at www.jacionline.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.jacionline.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable IV. Demographic characteristics of study groups in the oral microbiome.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN, subjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge, mean, years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge, range, years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFemale/males\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eControls\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u0026ndash;57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2/1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSJS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3/2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSjD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3/0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.308\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.726\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eP values were calculated using the Kruskal-Wallis \u003csup\u003e(a)\u003c/sup\u003e and Fisher test \u003csup\u003e(b)\u003c/sup\u003e. SJS, Stevens-Johnson syndrome; SjD, Sj\u0026ouml;gren\u0026rsquo;s disease.\u003c/p\u003e \u003cp\u003eRegarding alpha and beta diversity, as well as phyla and genera composition, a large similarity was observed across the SJS and SjD groups when compared to the healthy control group. \u003cem\u003eVeillonella\u003c/em\u003e showed significantly higher abundance in the SJS and SjD groups than healthy controls (p\u0026thinsp;=\u0026thinsp;0.047). Similarly, \u003cem\u003ePrevotella\u003c/em\u003e also showed higher abundance in both groups, although this difference was not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.184) (Table V).\u003c/p\u003e \u003cp\u003eTable V. Alfa diversity, phylum and genus abundance in each group in oral microbiome.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControls\u0026nbsp;(N\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSJS (N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSjD (N\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAlfa diversity, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Min-Max)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChao1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.67\u0026thinsp;\u0026plusmn;\u0026thinsp;36.02 (77.0 to 143.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.20\u0026thinsp;\u0026plusmn;\u0026thinsp;17.70 (66.0 to 104.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89 (72.00 to 77.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 (2.11 to 2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73 (1.87 to 3.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39 (2.10 to 2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhylum - Abundance (%), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Min-Max)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActinobacteriota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57 (0.80 to 5.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41 (1.50 to 8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36 (5.50 to 7.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteroidota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.70\u0026thinsp;\u0026plusmn;\u0026thinsp;5.25 (2.10 to 12.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.64\u0026thinsp;\u0026plusmn;\u0026thinsp;12.65 (4.00 to 35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.17\u0026thinsp;\u0026plusmn;\u0026thinsp;4.68 (3.80 to 12.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCampilobacterota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 (0.10 to 0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04 (0.30 to 2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 (0.30 to 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirmicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.60\u0026thinsp;\u0026plusmn;\u0026thinsp;8.55 (52.20 to 69.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.27 (35.20 to 74.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33 (71.5to 73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFusobacteriota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64 (2.70 to 3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53 (1.60 to 6.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12 (3.20 to 7.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatescibacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30 (0.10 to 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37 (0.20 to 3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 (0.10 to 0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.83\u0026thinsp;\u0026plusmn;\u0026thinsp;10.86 (14.40 to 35.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.50\u0026thinsp;\u0026plusmn;\u0026thinsp;3.69 (12.80 to 22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.30\u0026thinsp;\u0026plusmn;\u0026thinsp;7.01 (1.20 to 13.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpirochaetota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 (0.00 to 0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 (0.00 to 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 (0.00 to 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGenus - Abundance (%), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Min-Max)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVeillonella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60 (1.27 to 2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.62 (2.61 to 11.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62 (2.90 to 8.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrevotella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41 (0.66 to 3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.86\u0026thinsp;\u0026plusmn;\u0026thinsp;11.34 (3.14 to 29.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.64\u0026thinsp;\u0026plusmn;\u0026thinsp;5.09 (1.82 to 11.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eActinobacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 (0.00 to 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11 (0.00 to 4.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 (0.00 to 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eActinomyces\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49 (0.40 to 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94 (0.55 to 3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49 (1.47 to 2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAggregatibacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61 (0.24 to 3.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18 (0.00 to 2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82 (0.00 to 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAlloprevotella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69 (0.00 to 1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;2.73 (0.12 to 6.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 (0.00 to 0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFusobacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 (1.92 to 2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88 (1.43 to 5.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42 (0.86 to 7.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGemella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 (1.00 to 2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29 (0.00 to 3.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;5.82 (0.00 to 11.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHaemophilus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.98\u0026thinsp;\u0026plusmn;\u0026thinsp;9.34 (9.95 to 27.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56 (7.66 to 16.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;5.76 (0.16 to 10.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLeptotrichia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70 (0.64 to 1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39 (0.18 to 3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41 (0.00 to 2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNeisseria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52 (1.54 to 2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75 (0.22 to 2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24 (0.00 to 0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePorphyromonas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.81 (0.59 to 6.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34 (0.03 to 5.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 (0.13 to 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRothia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83 (0.26 to 3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92 (0.90 to 3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81 (2.1 to 5.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.11\u0026thinsp;\u0026plusmn;\u0026thinsp;7.10 (47.89 to 62.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.6\u0026thinsp;\u0026plusmn;\u0026thinsp;22.25 (13.67 to 67.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.27\u0026thinsp;\u0026plusmn;\u0026thinsp;10.22 (49.4 to 69.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eP values were calculated using the Kruskal-Wallis test. SJS, Stevens-Johnson syndrome; SjD, Sj\u0026ouml;gren\u0026rsquo;s disease.\u003c/p\u003e \u003cp\u003eTwo patients with severe ocular SJS (representing 100% of the severe group analyzed for both microbiomes) exhibited a markedly elevated abundance of \u003cem\u003ePrevotella\u003c/em\u003e in both the gut and oral microbiomes; however, this difference was not statistically significant. One patient showed \u003cem\u003ePrevotella\u003c/em\u003e accounting for 27.59% of the gut microbiome and 29.81% of the oral microbiome, while the other demonstrated \u003cem\u003ePrevotella\u003c/em\u003e comprising 12.63% of the gut microbiome and 8.41% of the oral microbiome.\u003c/p\u003e \u003cp\u003eTaken together, our findings indicate the presence of gut dysbiosis in SJS patients, characterized by a significantly reduced Chao1 index (p\u0026thinsp;=\u0026thinsp;0.012), with a progressive decline correlating with increased ocular surface severity. A reduction in \u003cem\u003eFaecalibacterium\u003c/em\u003e abundance was also observed (p\u0026thinsp;=\u0026thinsp;0.050). Additionally, correlations were identified between certain bacterial phyla and genera, including Christensenellaceae, Subdoligranulum, and \u003cem\u003eClostridia\u003c/em\u003e, and higher dry eye indices. Severe SJS cases showed elevated \u003cem\u003ePrevotella\u003c/em\u003e in both microbiomes.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study demonstrated that patients with SJS exhibit significant gut dysbiosis, characterized by reduced alpha diversity, as indicated by lower Chao1 indices, which progressively correlated with increased ocular surface severity. In addition, distinct alterations in gut microbial composition were observed when compared to healthy controls. While previous studies described ocular surface and lid margin dysbiosis in SJS,\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e our study is the first to simultaneously assess both gut and oral microbiomes in this patient population, while also comparing them with patients with primary SjD and healthy individuals. Furthermore, we examined correlations between gut microbial composition and dry eye parameters.\u003c/p\u003e \u003cp\u003eOur findings support the hypothesis that gut microbial dysbiosis contributes to the pathogenesis of SJS, as has been reported in various inflammatory and autoimmune diseases. This influence may occur through multiple pathways, including the gut\u0026ndash;eye axis, with particular microbial taxa in the gut being implicated in ocular disease. The intestinal commensal microorganism plays a critical role in limiting pathogen colonization and maintaining mucosal immune homeostasis throughout the body.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn this study, alpha-diversity was significantly reduced in SJS patients compared to healthy controls, and this reduction, particularly in the Chao1 index, correlated with increasing ocular inflammation and disease severity. This is consistent with findings in Low et al.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e in mucous membrane pemphigoid, another progressive autoimmune scarring disorder involving mucosal sites, including the ocular surface, and characterized by autoreactive T cells and cicatrizing conjunctivitis, paralleling aspects of SJS. Similarly, lower alpha-diversity has been reported in uveitis,\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Beh\u0026ccedil;et\u0026rsquo;s disease,\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e Sj\u0026ouml;gren\u0026rsquo;s disease,\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e diabetic retinopathy.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e Reduced gut microbial diversity is generally associated with higher levels of systemic inflammation and compromised intestinal barrier function, facilitating the expansion of pathogenic taxa and perpetuating immune activation. \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA notable finding in our cohort of SJS patients was the depletion of \u003cem\u003eFaecalibacterium\u003c/em\u003e, a key butyrate-producing genus with anti-inflammatory properties.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e This observation agrees with the findings in uveitis,\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e SjD \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and Beh\u0026ccedil;et's disease.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e In mucous membrane pemphigoid, Lower et al.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e also reported reduced alpha-diversity with increased ocular severity and depletion of butyrate-producing taxa. \u003cem\u003eFaecalibacterium\u003c/em\u003e plays a critical role in intestinal homeostasis by supporting barrier integrity, promoting tolerogenic dendritic cells and FOXP3\u0026thinsp;+\u0026thinsp;regulatory T cells (Tregs), and modulating cytokine production. Short-chain fatty acids (SCFAs), particularly butyrate, exert potent immunomodulatory effects and are largely derived from microbial fermentation of dietary starches.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Schaefer et al.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e demonstrated in both SjD patients and mouse models that intestinal commensals, such as \u003cem\u003eFaecalibacterium\u003c/em\u003e, can influence ocular surface health by promoting the development of Tregs in draining lymph nodes.\u003c/p\u003e \u003cp\u003eWe also observed an increased abundance of the pro-inflammatory genus \u003cem\u003ePrevotella\u003c/em\u003e in both gut and oral microbiome of SJS patients, particularly in severe cases. This finding is consistent with reports in uveitis,\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e diabetic retinopathy,\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e and SjD.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e Elevated \u003cem\u003ePrevotella\u003c/em\u003e abundance has been linked to dry eye severity. \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e \u003cem\u003ePrevotella\u003c/em\u003e is known to degrade mucin, disrupt the colonic barrier, and stimulate immune activation via Toll-like receptors TLR2 and TLR4, contributing to chronic inflammation and autoimmune pathology, including rheumatoid arthritis and immune-mediated dry eye.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn line with expectations, the SJS group exhibited worse dry eye outcomes, with DED DEWS scores ranging from 3 to 4 and lower microbial diversity correlating with ocular severity. These results align with those of de Paiva et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and Moon et al.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, who found that patients with more severe DED in SjD also had lower gut microbial diversity. Our study further correlated microbial composition at the phylum and genus levels with specific dry eye parameters. At the phylum level, increased abundance of Actinobacteria and Synergistota was associated with lower NEI scores, indicating less ocular surface damage. Moon et al.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e reported similar findings, linking Actinobacteria with improved TBUT and reduced dry eye severity, and Wang et al.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e found an association between Actinobacteria and reduced dry eye severity.\u003c/p\u003e \u003cp\u003eAt the genus level, \u003cem\u003eSubdoligranulum\u003c/em\u003e abundance was negatively correlated with NEI scores, consistent with reports by Cao et al.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and Moon et al.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, where this genus was depleted in both SjD and non-Sj\u0026ouml;gren dry eye groups. We also observed increased abundance of \u003cem\u003eChristensenellaceae\u003c/em\u003e correlated with lower DED DEWS scores, and \u003cem\u003eClostridia\u003c/em\u003e was positively associated with TBUT. These taxa may play a protective role and be associated with milder forms of DED in SJS.\u003c/p\u003e \u003cp\u003eIn our cohort, alpha-diversity (Chao1 and Shannon indices) did not differ significantly between the SjD and control group, consistent with findings from Mendez et al.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, Moon et al.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and Zhang et al.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, but diverging from those of Schaefer et al.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, de Paiva et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and Cano-Ortiz et al.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e Beta diversity and microbial composition at the phylum and genus levels were also largely similar to the healthy control group, with some variations that did not reach statistical significance. These discrepancies may stem from the small sample size, population differences (e.g., geography, diet, sex, age), and disease severity heterogeneity.\u003c/p\u003e \u003cp\u003eRegarding the oral microbiome, notable differences included elevated levels of \u003cem\u003eVeillonella\u003c/em\u003e and \u003cem\u003ePrevotella\u003c/em\u003e in both SJS and SjD patients compared to the control group. These genera have been linked to poor oral health and periodontal disease.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003eOur findings are in line with previous studies reporting minimal differences in oral microbial diversity between DED, SjD, and controls.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e Zhou et al.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e found increased Bacteroidete\u003cem\u003es\u003c/em\u003e and Firmicutes with reduced Proteobacteri\u003cem\u003ea\u003c/em\u003e in SjD oral samples.\u003c/p\u003e \u003cp\u003eDespite its strengths, this study has several limitations. It is a cross-sectional, observational study with a relatively small sample size, especially for a rare disease like SJS, and lacks longitudinal follow-up. The oral microbiome subgroup was limited, with only three patients in both the healthy and SjD groups. This was included to explore potential microbiome continuity along the digestive tract. Furthermore, while 16S rRNA sequencing provides valuable genus-level insights, it lacks the resolution for species or strain-level identification.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e More comprehensive approaches, such as shotgun metagenomics or whole-genome sequencing, could offer deeper insight into microbial functionality. Integration with host inflammatory markers and metabolomic profiling would further enhance mechanistic understanding.\u003c/p\u003e \u003cp\u003eIn summary, this study demonstrates that patients with SJS exhibit significant gut dysbiosis, with reduced microbial richness, depletion of anti-inflammatory bacteria (\u003cem\u003eFaecalibacterium\u003c/em\u003e), and enrichment of pro-inflammatory taxa (\u003cem\u003ePrevotella\u003c/em\u003e), particularly in severe cases. Notably, \u003cem\u003ePrevotella\u003c/em\u003e abundance was elevated in both gut and oral microbiomes of SJS patients. These findings highlight a complex interplay between the gut and oral microbiome and inflammatory pathways underlying SJS.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author (L.F.) upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLF wrote the original draft and made subsequent revisions, TTR contributed to the methodology and writing of the original draft reviewing and editing the final manuscript, AF contributed to the writing, reviewing and editing the final manuscript, RJAA contributed to the methodology, reviewing and editing the final manuscript, CSP contributed to the analysis, reviewing and editing the final manuscript, JAPG contributed to the design of the study, reviewing and editing the final manuscript. All authors approved the final version of the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. CSdP receives salary support from the Caroline Elles Professorship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Outside any subjects treated in the present work, \u003cstrong\u003eJosé Álvaro Pereira Gomes\u003c/strong\u003e 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 \u0026amp; 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.\u0026nbsp;\u003cstrong\u003eCintia S. de Paiva\u003c/strong\u003e has the following financial interests or relationships to disclose: HannAll: research grant. 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PMID: 38460758.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Stevens-Johnson syndrome, Sjögren's disease, gut microbiome, oral microbiome, dry eye","lastPublishedDoi":"10.21203/rs.3.rs-6516559/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6516559/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo investigate and compare the gut and oral microbiome in patients with Stevens-Johnson syndrome (SJS), Sj\u0026ouml;gren's disease (SjD), and healthy controls, using next-generation sequencing (NGS) and correlate with dry eye parameters. Fecal samples from ten SJS, ten SjD, and ten healthy controls were analyzed. Oral swabs were obtained from six SJS, three SjD, and three healthy controls. Dry eye parameters were employed to evaluate the dry eye disease (DED). Microbiome profiles were determined by next-generation sequencing of the 16S V3-V4 region and analyzed using the Silva database. The gut microbiome showed significant differences in the SJS group, including a reduced Chao 1 index (p\u0026thinsp;=\u0026thinsp;0.01) progressively correlated with increased ocular severity and decreased \u003cem\u003eFaecalibacterium\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.05) compared to the respective healthy control group. Severe SJS cases showed elevated \u003cem\u003ePrevotella\u003c/em\u003e in both microbiomes. Strong correlations were observed in SJS between \u003cem\u003eChristensenellaceae\u003c/em\u003e and DEWS score (p\u0026thinsp;=\u0026thinsp;0.04), \u003cem\u003eSubdoligranulum\u003c/em\u003e and NEI score (p\u0026thinsp;=\u0026thinsp;0.04), and \u003cem\u003eClostridia\u003c/em\u003e with TBUT (p\u0026thinsp;=\u0026thinsp;0.009). In SjD, gut profiles resembled healthy controls. The oral microbiome was similar across groups, except for higher \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eVeillonella\u003c/em\u003e levels in SJS and SjD patients. SJS patients exhibited gut dysbiosis, characterized by reduced microbial richness and a significant decrease in the abundance of \u003cem\u003eFaecalibacterium.\u003c/em\u003e Certain bacterial genera were correlated with the severity of dry eye, suggesting a potential link between gut microbiome alterations and the clinical progression of the disease.\u003c/p\u003e","manuscriptTitle":"Analysis of the gut and oral microbiome in patients with Stevens-Johnson syndrome and Sjögren’s disease: associations with dry eye severity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 14:53:19","doi":"10.21203/rs.3.rs-6516559/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e2eb582-8b3e-45ca-90a0-49edae69b64c","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48691085,"name":"Biological sciences/Immunology"},{"id":48691086,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-06-10T18:53:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-20 14:53:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6516559","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6516559","identity":"rs-6516559","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}