Gut Dysbiosis in a Murine Model of Cutaneous Lupus Erythematosus Correlates with Infiltration of Antigen-Specific T cells and Antigen Presenting Cells in Skin

Gut Dysbiosis in a Murine Model of Cutaneous Lupus Erythematosus Correlates with Infiltration of Antigen-Specific T cells and Antigen Presenting Cells in Skin | 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 Gut Dysbiosis in a Murine Model of Cutaneous Lupus Erythematosus Correlates with Infiltration of Antigen-Specific T cells and Antigen Presenting Cells in Skin Haley Neff, Ümmügülsüm Yıldız-Altay, Nuha Salam, Doyle V. Ward, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7023225/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract The commensal organisms constituting the human microbiome are increasingly appreciated to fortify epithelial barriers and modulate host immunity. Dysbiosis of both single strains and communities can contribute to inflammatory conditions. Here, we sought to characterize potential dysbiosis in our inducible mouse model of cutaneous lupus erythematosus (CLE). We hypothesized that gut dysbiosis would occur based on several studies that found lower Firmicutes/Bacteroidetes (F/B) ratios and decreased diversity in systemic lupus erythematosus (SLE) cohorts compared to healthy counterparts, a mouse study that identified Ro60 commensal orthologs that can trigger onset of lupus-like disease, and a study of CLE that identified outgrowth of Staphylococcus aureus in the skin. Using whole genome shotgun sequencing, we identified differences in pre- and post-irradiation cohorts, particularly an increase in Duncaniella , a decrease in Prevotella , and a reduction in alpha diversity following irradiation. Baseline alterations in CLE mice gut bacteria compared to littermate controls were also extant, including trends toward increased Parabacterides distasonis and Bacteroides acidifaciens in CLE mice. Importantly, we noted an increase in Phocaeicola sartorii in CLE mice compared to littermate controls post-disease induction. We examined the mycobiome in our mice and noted a reduction of Colletotrichum tofieldiae specifically in CLE mice post-disease induction, and a trend towards increased Periglandula ipomoeae . Last, we correlated abundance of genera and species with flow cytometry data obtained from the skin, lymph node and spleen, and identified specific strains that correlated with presence of antigen-specific T cells and different antigen presenting cell populations. Thus, our model exhibits similar changes to other models of lupus-like disease, and our data identify potential novel strains/species that could be modified for CLE and/or SLE treatment such as through generation of probiotics or specific antimicrobial agents. Biological sciences/Immunology Biological sciences/Microbiology lupus microbiome mycobiome autoimmune shotgun sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Lupus is a complex and multifactorial disease process attributed to aberrant immune activation against the self, resulting in a heteromorphic symptomatology and several subtypes with varying organ involvement. Most lupus patients present with autoantibodies produced by B cells, autoreactive T cell subsets, and abnormally elevated levels of inflammatory cytokines, hallmarks of both innate and adaptive immune dysregulation 1 . Lupus impacting mucosal tissue or cutaneous tissue is termed cutaneous lupus erythematosus (CLE) 2 , which has an incidence of 4.2–4.3 per 100,000 in the US 3 . CLE and SLE are interrelated diseases: CLE occurs in as many as 80% of SLE patients, and patients that present with isolated CLE can progress to SLE. Adequate control of CLE mitigates the risk of systemic progression 4 , whereas skin damage induced by UV light can exacerbate lupus nephritis 5 . The microbiome is a relatively new area of investigation for targeted therapy in lupus and other autoimmune diseases 1 . Gut microbiota have been heavily implicated in modulating activities of the immune system, including both innate and adaptive immune system activation and regulation 6 . Microbiota also play a role in the regulation of T cell maturation, and especially the activity of T regulatory cells, which are notably awry in autoimmune diseases 7 . Dysbiosis, which is defined by a loss of beneficial microbial populations, an increase in deleterious bacterial species, and/or an inappropriately low microbial community diversity, can impair gut and skin barrier function, increasing permeability and thus exposure to the external environment. Dysbiosis has been reported in both lupus-prone mice and human systemic lupus patients 8 – 10 . Dysbiosis can also trigger autoimmunity directly through molecular mimicry. In the case of lupus, Ro60 orthologues trigger onset of lupus in mice 11 . Few studies have been performed specifically on CLE, though S. aureus outgrowth has been characterized in lesional skin 12 . A subsequent study demonstrated that skin S. aureus colonization could contribute to SLE, increasing glomerulonephritis and autoantibody deposition, in genetically-prone animals 13 . Here, we sought to characterize the gut microbiome in our novel B6 mouse model of CLE 14 . This model employs a TLR9KO lupus-prone background, with a keratin 5-driven ovalbumin under the control of a tetracycline response element. This genetic system is inducible and allows for flares. When these recipient mice are injected with Th2-skewed antigen-specific OT2 T cells, mice develop skin lesions that are clinically and histologically similar to human lupus, as well as splenomegaly and positive ANA 15 . We used whole genome shotgun sequencing to analyze fecal pellets from these animals, identifying several bacterial and fungal species that could be targeted for future lupus therapeutic development. Results The gut microbiome in CLE mice has a lower alpha diversity, and disease induction induces a loss of Prevotella and an outgrowth of Duncaniella First, we compared fecal microbiome samples from CLE and littermate control mice, both pre- and post-disease induction ( Fig 1A ). Sex and cage number did not significantly cluster on PCA ( Fig. 1B-C ), therefore samples were pooled for analysis by time (pre- versus post-induction) and genotype status (littermates versus CLE). Based on the weighted unique fraction (unifrac) distance matrix, which takes into account phylogenetic relatedness as well as relative abundance, many pre-induction littermates and pre-induction CLE-prone mice were most similar to each other, though not all post-induction CLE mice were most similar to other post-induction CLE mice, occasionally seeming more similar to their post-induction littermates ( Fig. 1D ). Firmicutes/Bacteroidetes (F/B) ratios did not significantly differ amongst littermates and their CLE counterparts, though there was a trend towards increased Bacillota and reduced Bacteroidota (formerly Firmicutes) in CLE mice ( Fig. 1E-F ). We also observed proportional changes in abundance of specific taxa in irradiated CLE compared to irradiated littermate mice ( Fig S1 ). Next, we assessed grouped analyses for pre- and post-induction CLE mice and littermates. Mice with more severe skin lesions tended to have fewer Prevotella at the species and genus level ( Fig. S2 ). CLE mice also had the lowest alpha diversity ( Fig 2C ) and trended towards lower beta diversity using Shannon’s Index but not Simpson’s ( Fig 2D-E ). Examination of differences in pre- versus post-induction revealed a significant increase in Duncaniella , Bacteroidales and Lepagella species, ( Fig 2A-B, 3D, S3 )and a decrease in Prevotella ( Fig 2A-B ) . Taken together, these data demonstrate that mouse genotype and disease induction process (irradiation, doxycycline administration, T cell injection) influence the gut microbiome in the CLE mouse model, and that diseased animals have a lower alpha diversity. P. sartorii is significantly increased in CLE mice compared to littermates, and Bacteroides, Parabacteroides, Duncaniella and Blautia increase with disease whereas Alistipes decreases We next asked whether we could identify specific strains that change with disease status, with the goal of identifying potential treatment targets or probiotics. Examining the top 10 species by disease group identified stepwise increases in Bacteroides , Parabacteroides and Duncaniella , with the lowest abundance in pre-induction littermates and the highest abundance in post-induction CLE mice ( Fig 3A-D ). Examining the top 10 strains by disease group identified a similar stepwise increase in Blautia pseudococcoides , and a stepwise decrease in Alistipes MGBC116833 ( Fig 3E-G ). Phocaeicola sartorii was one of the few strains that was significantly increased in post-induction CLE versus littermate mice ( Fig 3H ). Taken together, these data identify specific bacterial strains that might be useful targets for development of probiotics or strain-specific lysis such as through bacteriophage therapy. Specific strains correlate with immune infiltrates in skin as well as abundance in lymphoid organs To understand the potential impact the gut microbiome could have on skin disease in our lupus model, we examined immune data across disease cohorts and correlated these data with microbiome data. We gated immune cells in the lymph node (LN), spleen (SP) and skin (SK) in post-induction littermates and CLE mice to identify which populations were significantly different in diseased animals ( Fig 4A, Table S1 ). Next, we correlated these significantly different immune cell populations with the top microbiome genera, namely host T cells in the lymph node, mouse weight, host T cells in the spleen, CD45 cells in the spleen, B cells in the spleen, spleen weight (splenomegaly), F4-80 Ly6Clo monocytes/macrophages in the lymph node, skin score, CD45 cells in the skin, F4-80 macrophages in the lymph node, and antigen specific T cells in the lymph node and skin ( Fig 4B ). Heatmap matrix of bacteria genera and key immune data showed associations of higher Bacteroides, Parabacteroides, Duncaniella with higher skin scores, spleen weights, and specific cell lines related to disease, whereas Akkermansia, Lawsonibacter, Lactobacillus, Schaedlerella , and Odoribacter had negative associations with disease related flow data. Next, we examined strains that correlated with specific immune cell populations as identified in our correlation matrix, which we present in Fig 4C-H . Specifically, we found positive correlations between Parabacterodies distasonis and Duncaniella muris with antigen-specific T cells in the LN and SK; Phocaeicola sartorii with F4-80 Ly6Clo monocytes/macrophages in the LN and monocytes in the SK; Bacteroidales bacterium M13 with antigen-specific T cells in the SK and F4-80 Ly6Clo monocytes/macrophages in LN; Bacteroides acidifaciens with antigen-specific T cells and monocytes in SK; and Flavonifractor with total CD45+ and neutrophils (PMN) in SK. We also performed functional gene analyses on the bacterial strains and noted an increase in the term “integral membrane protein functional pathways” in CLE post-induction mice ( Fig 5 ). We hypothesize that this may relate to immune cell activation, as synthesis of bacterial membrane proteins, including upregulation of receptors, transport and membrane binding components, contribute to immune cell activation and extravasation to skin. Loss of specific fungal strains is associated with CLE disease We also compared fungi abundance across cohorts, and expressed reads as percentages of overall number of classified reads for respective subjects. Fungi read counts were overall low in gut samples (<100); therefore, the data is of limited internal validity and was insufficient to calculate abundance by OneCodex. Overall trends showed lower proportions of Colletotrichum tofieldiae (p<0.05) and Verticillium in CLE gut microbiome compared to littermates, and an increased proportions of Periglandula ipomoeae and Penicillium paneum in CLE compared to littermates, though these changes were similar to differences in pre-irradiation CLE mice versus littermates ( Fig. 6 ). Taken together, these data demonstrate the effect that genotype and disease induction (irradiation, doxycycline administration, T cell injection) has on the gut mycobiome. Discussion Current first line treatment SLE or CLE is largely nonspecific immune suppression, increasing risk of infections, cancers, and other short- and long-term side effects. While changes in the microbiome have been investigated in both murine models and humans with SLE, limited research has focused on potential probiotic interventions and the impact of fungal communities on lupus. Significant and consistent microbiome differences across lupus mouse models and more recent human studies support a promising role for dietary interventions that increase microbial diversity and decrease inflammation 6 . Thus, the characterization of the microbiome and mycobiome can set the groundwork for further mechanistic and interventional study. We found no significant differences in F/B ratios from our mice, though normalized read counts of the top 10 phyla demonstrated an increase in Bacillota and a decrease in Bacteroidota in CLE mice post-induction. Interestingly, these findings are consistent with other SLE murine models in which there was no significant difference in F/B ratios between healthy and control mice, despite there being notable changes in the gut microbiome associated with disease progression and remission 16 . Literature suggests that F/B ratios are lower in mice than in humans and caution is needed when generalizing F/B ratios in model organisms as they may not reliably recapitulate the human gut microbiome 17 . Though alpha diversity trended lower, beta diversity by Simpson index was similar across cohorts. Notably, Shannon beta diversity, which gives more weight to rare species, was more different between CLE mice and littermates, indicating greater reductions in more rare species in diseased mice. The model induction process, which involves administration of doxycycline chow to turn on the model autoantigen and 400R irradiation to make room for antigen-specific T cells, impacts the microbiome in both CLE mice and littermate controls. Specifically, we noted a significant loss of Prevotella , which is also observed to be lost in human gut microbiomes in response to Western diet 18 . Prevotella copri was recently reported to be lost in Korean SLE patient gut microbiomes 19 . Prevotella is sensitive to doxycycline, though it is unclear whether the dose administered to the mice (20mg/kg ad libitum) was responsible for the observed loss as long-term doxycycline administration at 20mg sub-antimicrobial doses has been reported to not have significant effects on gut or vaginal microbiota in humans 20 . Low dose irradiation was reported to reduce alpha diversity in mice, as well as specific metabolites 21 . It would be interesting to study whether the specific loss of Prevotella and reduced alpha diversity is sufficient to allow for the development of cutaneous lupus in mice as a result of the disease induction protocol. We found significant increases in Bacteroides, Parabaceteroides, Duncaniella, Blautia pseudococcoides and Phocaeicola sartorii in our post-induction CLE mice compared to other groups, indicating that these strains might be associated with lupus inflammation. Bacteroides and Parabactererodies species are increased in human lupus gut microbiomes, specifically in glucocorticoid negative patients 22 . This fits with our mouse model, as we did not provide glucocorticoids to the mice. Duncaniella was increased in a study that performed fecal transplantation through feeding of WT or control (non-diseased) mouse fecal pellets to SLE-prone mice 16 . It is possible that our mice had increases in Duncaniella through coprophagy from littermates that were housed together in their cages. However, it is unclear why they would have higher levels compared to littermates, unless there is an effect of genotype and/or inflammatory status on abundance of this phylum. A recent study characterizing microbiome changes in lupus patients identified Blautia gnavus blooms during flares 23 . B. gnavus used to be considered a part of the B. pseudococcoides family but was recently reclassified 24 . It would be interesting to further investigate whether or not these strains perform homologous functions in the context of lupus across species. P. sartorii was recently reported to be enriched in the gut of mild, but not severe, lupus in MRL/lpr mice 25 . Given that our model primarily impacts the skin, with mild if any kidney disease, this fits with this observation. Antigen-specific T cell infiltration into the skin was positively correlated with Parabacteroides distasonis, Duncaniella muris, Bacterodiales bacterium M13 and Bacteroides acidifaciens . P. distasonis was recently reported to promote CXCL9 secretion by tumor-associated macrophages which in turn promoted CD8 + T cell activation and anti-tumor immunity in the context of lung cancer 26 . How this activity could influence skin infiltration of T cells in an autoimmune setting requires further investigation. D. muris was recently described to have different clinical isolates 27 , one of which is associated with anti-inflammatory properties in a DSS colitis model 28 . It is unclear whether the isolate in our mice is pro- or anti-inflammatory, though our data suggests that it might act in a pro-inflammatory capacity in the TLR9KO genetic background, given the increase in antigen-specific T cells in the skin. Antigen presenting cell populations’ infiltration into the skin, including monocytes (Ly6C + Ly6G-) and F4-80 + Ly6Clo macrophages, were positively correlated with P. sartorii and B. acidifaciens , with higher lymph node numbers positively correlated with P. sartorii and B. bacterium M13 . How P. sartorii or B. bacterium M13 impact monocyte and macrophage populations remain unclear. B. acidifaciens is associated with inflammation-induced tumorigenesis in DSS models, though the effect it might exert on tumor-associated macrophages is unclear 29 . Last, neutrophils and total CD45 + inflammatory cells were positively correlated with Flavonifractor . Flavonifractor is increased in the gut of bullous pemphigoid patients, providing a gut-skin connection with relevance to a skin disease that is also characterized by neutrophilic infiltration 30 . It would be interesting to test targeting of each of these strains in CLE to determine the impact on skin inflammation and clinical disease scores. This could be achieved through bacteriophage therapy, which is currently being investigated primarily for infectious diseases 31 . One specific example of this is a preclinical phage therapy that improved immunity to S. aureus in immunocompromised mice 32 . Given that S. aureus is enriched in CLE skin 12 , this could be a promising therapeutic for lupus skin disease. Microbiota have been investigated to impact metabolism through augmentation of human metabolic pathways, a well-known symbiotic relationship in which bacteria supply enzymatic capabilities its host would otherwise not have 33 . Functional data in our model showed an upregulation of membrane proteins, which includes transporter families and cellular attachment and signaling proteins such as lectins and binding receptors. All of these ligands have the potential to stimulate innate immune receptors, which could contribute to lupus immunopathogenesis 34 . Studies have also demonstrated changes in the gut’s fungal populations, or the mycobiome, in SLE 35 . One pilot study found human SLE mycobiomes demonstrated distinct dysbiosis compared to healthy controls and rheumatoid arthritis 36 . Another study reported different mycobiome beta diversities in three cohorts of SLE with lupus nephritis (LN), SLE without LN, and healthy controls, with both SLE cohorts having increased ratio of opportunistic fungi and Aspergillus being correlated with 24 hour proteinuria, anti-dsDNA and ANA 37 . Murine lupus models including FcGRIIb deficient mice and pristane treated mice exhibit an elevated Basidiomycota-to-Ascomycota ratio that was positively correlated with disease severity 38 . In our model, the fungal strain Penicillium paneum was higher in CLE mice versus littermates, while Colletotrichum tofieldiae was absent. Although low fungal read yields limit interpretation of this data, we hypothesize that fungal probiotics that are lacking in CLE post-induction mice could be further tested for utility as probiotic strains. Colletotrichum tofieldiae is a particularly interesting candidate given it is considered a beneficial root probiotic 39 , and might be enriched in whole food diets 40 . Limitations of our study include small sample size which limited power to detect significant differences. Mice were selected from several experiments that occurred at separate times, in a clean but not germ-free environment. Intermouse grooming and coprophagia may introduce microbiome congruence across disease cohorts. Males and females were analyzed together based on our PCA analysis, though sex hormones have been shown to play a role in microbiome composition in other murine models. Last, while analysis of the gut microbiome in murine models is useful for hypothesis generation and particularly for interventional studies involving the microbiome, the mouse microbiome cannot be directly extrapolated to humans. In conclusion, we present this paper as a characterization of a CLE mouse model’s gut microbiome for future interventional study, as well as for hypothesis generation in further study in humans. Future directions include investigation of the skin microbiome in this model, as well as mechanistic studies understanding how specific strains and populations impact disease progression are planned. Materials & Methods Mouse model & fecal pellet sampling Animal protocol was reviewed and approved by the UMass Chan IACUC (#202100229), and studies were conducted in accordance with the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals. Eighteen (18) mice were selected for analysis from a representative cohort of CLE mice and littermate controls from 2–3 separate experiments. Disease was induced as previously described 14 , 15 using 10 million Th2 skewed OT2 CD4 + T cells adoptively transferred into sublethally irradiated recipient mice (400R). Mice also received 200mg/kg doxycycline chow (BioServ) ad libitium to turn on the ovalbumin model autoantigen in keratin 5 expressing cells (keratinocytes). Severity of skin lesions were collected weekly on a 0–4 scale, with 0 indicating no skin changes and 4 indicating at least 75% skin involvement. Stool samples from pre- (week 0) and post-induction (week 3–4) were snap frozen at -70C and batched for analysis. We confirm the study design is reported in accordance with ARRIVE guidelines. Microbiome sample preparation Transnetyx Microbiome kits containing barcoded sample collection tubes were provided by Transnetyx (Cordova, TN, USA). Mouse fecal samples were placed in individual tubes containing DNA stabilization buffer to ensure reproducibility, stability, and traceability, and shipped for DNA extraction, library preparation, and sequencing by Transnetyx (Cordova, TN USA). DNA Extraction and Metagenomic Sequencing DNA extraction was optimized and fully automated using a robust process for reproducible extraction of inhibitor-free, high molecular weight genomic DNA that captures the true microbial diversity of stool samples. After DNA extraction and quality control (QC), genomic DNA was converted into sequencing libraries using a method optimized for minimal bias. Unique dual indexed (UDI) adapters were used to ensure that reads and/or organisms are not mis-assigned. After QC, the libraries were sequenced using the shotgun sequencing method (a depth of 2 million 2x150 bp read pairs), which enables species and strain level taxonomic resolution. Sequencing data were uploaded automatically onto One Codex analysis software and aligned against the One Codex database consisting of ~ 148K complete microbial genomes, including 71K distinct bacterial genomes, 72K viral genomes, and thousands of archaeal and eukaryotic genomes. The classification results were filtered through several statistical post-processing steps designed to eliminate false positive results caused by contamination or sequencing artifacts. Samples were compared with TransnetYX’s global diversity averages from historic data for quality control check. Low readcounts were proportionalized to the total number of identifiable reads from their host sample. Sequencing data was aligned with the Gene Ontology (GO) and KEGG Orthology databases for functional analysis on the OneCodex platform. Statistical analysis OneCodex python package, Jupiter notebooks, and GraphPad Prism were used for analysis. All data are displayed as mean ± SD. Multivariate analyses of skin score versus relative abundance of bacteria were repeated at different taxonomic levels using GraphPad Prism. Taxa with significant p-value correlation to skin score were extracted to compare across cohorts. Gaussian distribution was verified using the Anderson-Darling test. One-way ANOVAs with posttests were performed to compare all 4 groups (littermates pre-induction, littermates post-induction, CLE pre-induction, CLE post-induction), and normality tests and tests for standard deviation variations were conducted to ensure the correct type of ANOVAs and post-tests were selected. We also employed an unpaired t-test with Welch’s correction to compare between littermates and CLE mice post-disease induction when data were normally distributed. Mann-Whitney test was used when data were non-normally distributed. Network analyses are constructed by OneCodex package on Jupytr and Morpheus. Declarations Funding : Supported by a Lupus Research Alliance Mechanisms & Targets Award (JMR), UMass Chan startup funds (JMR) and UMass Chan Research & Curriculum Exploration summer stipend (HN). Acknowledgements : We thank Dr. Maldonado-Contreras and Dr. McCormick for their advice, expertise and enthusiasm in microbiome research. We also thank Denise and Austin at OneCodex for helping with Python, and Samira at TransNetYX for all her guidance with sample preparation and analyses. Conflicts of Interest : JMR is an inventor on patent application #62489191 and #15/851,651 which covers IL-15 and CXCR3 for the treatment of vitiligo, respectively; and on patent #63/478,900 filed for “Diagnosis of skin diseases in veterinary and human patients” for CTCL. The other authors have no conflicts of interest to disclose. Data availability statement: The datasets generated and/or analysed during the current study are available in the OneCodex repository, available via the following links: Gut microbiome project: https://app.onecodex.com/projects/120f7b4173934b33 Unifrac and heatmaps Jupytr notebook: https://app.onecodex.com/notebooks/public/160b861a6f8d4910 PCAs and alpha/beta diversity Jupytr notebook: https://app.onecodex.com/notebooks/public/fc7ddac768c645a2 . CREdiT statement : Conceptualization: JMR, HN Methodology: JMR Software: N/A Validation: DW, DS, ZGRO Formal analysis: HN, JMR, NS Investigation: UYA, HN Resources: JMR Data curation: HN, JMR Writing – original draft: HN, JMR Writing – review & editing: all authors Visualization: HN Supervision: JMR Project administration: JMR Funding acquisition: JMR, HN References Guo, X. et al. The Microbiota in Systemic Lupus Erythematosus: An Update on the Potential Function of Probiotics. Front. Pharmacol. 12 , 759095 (2021). Garelli, C. J. et al. 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Gut Microbes . 3 , 289–306 (2012). Tsokos, G. C. The immunology of systemic lupus erythematosus. Nat. Immunol. 25 , 1332–1343 (2024). Silverman, G. J., Azzouz, D. F., Gisch, N. & Amarnani, A. The gut microbiome in systemic lupus erythematosus: lessons from rheumatic fever. Nat. Rev. Rheumatol. 20 , 143–157 (2024). Li, B. Z. et al. Altered gut fungi in systemic lupus erythematosus - A pilot study. Front. Microbiol. 13 , 1031079 (2022). Yang, P. et al. Fungal gut microbiota dysbiosis in systemic lupus erythematosus. Front. Microbiol. 14 , 1149311 (2023). Cheibchalard, T. et al. Fungal microbiome in gut of systemic lupus erythematosus (SLE)-prone mice (pristane and FCGRIIb deficiency), a possible impact of fungi in lupus. PLoS One . 19 , e0314662 (2024). Hiruma, K. et al. Root endophyte Colletotrichum tofieldiae confers plant fitness benefits that are phosphate status dependent. Cell 165 , 464–474 (2016). Soto-Giron, M. J. et al. The Edible Plant Microbiome represents a diverse genetic reservoir with functional potential in the human host. Sci. Rep. 11 , 24017 (2021). Additional Declarations Competing interest reported. JMR is an inventor on patent application #62489191 and #15/851,651 which covers IL-15 and CXCR3 for the treatment of vitiligo, respectively; and on patent #63/478,900 filed for “Diagnosis of skin diseases in veterinary and human patients” for CTCL. The other authors have no conflicts of interest to disclose. Supplementary Files Neffetallsupplement.docx TableS1Immunedatainirradiatedmice.csv Cite Share Download PDF Status: Published Journal Publication published 12 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Sep, 2025 Reviews received at journal 17 Sep, 2025 Reviews received at journal 17 Sep, 2025 Reviews received at journal 13 Sep, 2025 Reviews received at journal 07 Sep, 2025 Reviewers agreed at journal 23 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviewers invited by journal 17 Aug, 2025 Editor assigned by journal 29 Jul, 2025 Submission checks completed at journal 18 Jul, 2025 First submitted to journal 18 Jul, 2025 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. 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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-7023225","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":502005338,"identity":"9e259b61-3563-406c-a962-efe13a10c4d4","order_by":0,"name":"Haley Neff","email":"","orcid":"","institution":"UMass Chan Medical School","correspondingAuthor":false,"prefix":"","firstName":"Haley","middleName":"","lastName":"Neff","suffix":""},{"id":502005339,"identity":"82a9b6ae-5303-457b-bd04-5b564e08b949","order_by":1,"name":"Ümmügülsüm Yıldız-Altay","email":"","orcid":"","institution":"UMass Chan Medical School","correspondingAuthor":false,"prefix":"","firstName":"Ümmügülsüm","middleName":"","lastName":"Yıldız-Altay","suffix":""},{"id":502005340,"identity":"6d87f17f-3a03-4c06-a680-e40b0e7ddbe7","order_by":2,"name":"Nuha Salam","email":"","orcid":"","institution":"UMass Chan Medical School","correspondingAuthor":false,"prefix":"","firstName":"Nuha","middleName":"","lastName":"Salam","suffix":""},{"id":502005341,"identity":"f5eede25-8688-4498-a256-e51072b1dfb6","order_by":3,"name":"Doyle V. Ward","email":"","orcid":"","institution":"UMass Chan Medical School","correspondingAuthor":false,"prefix":"","firstName":"Doyle","middleName":"V.","lastName":"Ward","suffix":""},{"id":502005343,"identity":"e182163d-38f3-47ab-bae7-096ca174c109","order_by":4,"name":"Dominique Shepard","email":"","orcid":"","institution":"UMass Chan Medical School","correspondingAuthor":false,"prefix":"","firstName":"Dominique","middleName":"","lastName":"Shepard","suffix":""},{"id":502005344,"identity":"9f9c1e3b-3673-4090-ad1d-278354751e88","order_by":5,"name":"Zaida G Ramirez-Ortiz","email":"","orcid":"","institution":"UMass Chan Medical School","correspondingAuthor":false,"prefix":"","firstName":"Zaida","middleName":"G","lastName":"Ramirez-Ortiz","suffix":""},{"id":502005345,"identity":"4b5f091c-5a91-4a39-9bcd-6a0dbd48ad16","order_by":6,"name":"Jillian M Richmond","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACxmbmxgMJIBYzAxtYoB9IfADLHcClhbEBVcvMNgbGGQfwaAGqaYBJQbRsOEZAC3M7UMuDmjsM5u3Mzx7dqLgju/l+j2Hzhz8Mcnw3EvA47NgzBpnDbObGOWeeGW87xmPYcLCNwVgSrxa2wwwSzDxs0rlthxOBWswfHGxgSNyAV8s/JC2b24C2HPjDUI9XS2IbkpYNbCAtbAwJBni19B3mkWBmM5POOXPYeMaxtMKGs20ShjPPPMCqxbD/8MGHP74dlpPgP/xMOqfisGx/8+GNDRV/bOT5jmO3xbABQvOgS0hgVQ4C8jhlRsEoGAWjYBTAAADAkWkZ53pFzQAAAABJRU5ErkJggg==","orcid":"","institution":"UMass Chan Medical School","correspondingAuthor":true,"prefix":"","firstName":"Jillian","middleName":"M","lastName":"Richmond","suffix":""}],"badges":[],"createdAt":"2025-07-01 19:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7023225/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7023225/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-34741-6","type":"published","date":"2026-01-12T16:28:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89351032,"identity":"f8b48688-7623-4802-ba1c-22fe59fb5fbc","added_by":"auto","created_at":"2025-08-19 06:10:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142869,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetrics of relatedness between samples show clustering of irradiated CLE and littermate mice, and no significant difference in F/B ratios\u003c/strong\u003e. \u003cstrong\u003eA\u003c/strong\u003e. PCA plot showing distribution by disease status. \u003cstrong\u003eB\u003c/strong\u003e. PCA plot showing sex differences and \u003cstrong\u003eC\u003c/strong\u003e. cage differences in the model. \u003cstrong\u003eD\u003c/strong\u003e. Weighted unique fraction matrix displaying relatedness between samples. \u003cstrong\u003eE\u003c/strong\u003e. Relative abundance bar graph of the top 10 phyla by disease status demonstrating increased Bacillota and reduced Bacteroidota in CLE mice. Legend displays phyla with NCBI Taxonomic ID number. \u003cstrong\u003eF\u003c/strong\u003e. F/B ratios calculated from normalized read counts. Pre-induction mice pooled from n = 2 experiments, n = 4 pre-induction littermates and n = 6 pre-induction CLE mice. Post-induction littermates pooled from n = 3 experiments, n = 9 post induction littermates and n = 9 post-induction CLE mice.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7023225/v1/b60c5ff951acd08f8d62c026.png"},{"id":89352189,"identity":"6c05dd8e-dff4-4df7-8343-e5fc2a35da52","added_by":"auto","created_at":"2025-08-19 06:34:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":248225,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReduced alpha diversity in CLE mice\u003c/strong\u003e. \u003cstrong\u003eA\u003c/strong\u003e. Relative abundance plots of the top 30 most common species and \u003cstrong\u003eB\u003c/strong\u003e. genera across all samples grouped by disease cohort. Legend displays species or genus name with NCBI Taxonomic ID number.\u003cstrong\u003e C\u003c/strong\u003e. alpha diversity, \u003cstrong\u003eD\u003c/strong\u003e. Simpson beta diversity, and \u003cstrong\u003eE.\u003c/strong\u003eShannon beta diversity by disease status. Pre-induction mice pooled from n= 2 experiments, n = 4 pre-induction littermates and n = 6 pre-induction CLE mice. Post-induction littermates pooled from n = 3 experiments, n = 9 post induction littermates and n = 9 post-induction CLE mice.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7023225/v1/b2bdaa63b6501668e300e920.png"},{"id":89351036,"identity":"002a6cfd-a6d1-4a09-baa9-dfce853a001f","added_by":"auto","created_at":"2025-08-19 06:10:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":259047,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExamination of species and strains significantly increased in CLE mice compared to irradiated littermates.\u003c/strong\u003e. Heatmap of top 10 genera by disease group. \u003cstrong\u003eB\u003c/strong\u003e. \u003cem\u003eBacteroides\u003c/em\u003e, \u003cstrong\u003eC\u003c/strong\u003e. \u003cem\u003eParabacteroides\u003c/em\u003e and \u003cstrong\u003eD\u003c/strong\u003e. \u003cem\u003eDuncaniella \u003c/em\u003eserve as examples of species that are enriched in post-induction CLE mice. \u003cstrong\u003eE.\u003c/strong\u003e Heatmap of top 10 strains by disease group.\u003cstrong\u003e F\u003c/strong\u003e. \u003cem\u003eBlautia pseudococcodies\u003c/em\u003e is increased in post-induction CLE mice. \u003cstrong\u003eG\u003c/strong\u003e. \u003cem\u003eAlistipes MGBC116833\u003c/em\u003e is decreased in post-induction CLE mice. \u003cstrong\u003eH\u003c/strong\u003e. Normalized abundance of \u003cem\u003ePhocaeicola sartorii \u003c/em\u003e(p\u0026lt;0.05) by disease group. (One-way ANOVAs with posttests significant as indicated; panel H demonstrates a t test for \u003cem\u003eP. sartorii \u003c/em\u003elittermates versus CLE mice as it was identified in a larger correlation matrix screen and was thus specifically tested for differences across post-induction genotypes). Pre-induction mice pooled from n= 2 experiments, n = 4 pre-induction littermates and n = 6 pre-induction CLE mice. Post-induction littermates pooled from n = 3 experiments, n = 9 post induction littermates and n = 9 post-induction CLE mice.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7023225/v1/abd6b6dda812b9f4d3208eb4.png"},{"id":89351208,"identity":"f7475193-ed4b-49e4-932b-f99316ca0058","added_by":"auto","created_at":"2025-08-19 06:18:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":421628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune data correlates with disease status, and specific species correlate with a higher abundance of disease-related immune cell subsets lymph nodes and skin\u003c/strong\u003e. \u003cstrong\u003eA\u003c/strong\u003e. Quantified flow cytometry data by experimental irradiated groups. \u003cstrong\u003eB\u003c/strong\u003e. Heatmap of significant flow cytometric parameters versus spleen weight, mouse weight, and genera sorted by 1-pearson coefficient on columns. \u003cstrong\u003eC. \u003c/strong\u003eThy1 T cells in lymph nodes and spleen versus \u003cem\u003eP. distasonis\u003c/em\u003e. \u003cstrong\u003eD. \u003c/strong\u003eThy1 cells in lymph nodes, skin versus \u003cem\u003eD. muris.\u003c/em\u003e \u003cstrong\u003eE\u003c/strong\u003e. F4-80 Ly6C cells in lymph nodes, monocytes in skin versus \u003cem\u003eP. sartorii. \u003c/em\u003e\u003cstrong\u003eF. \u003c/strong\u003eF4-80 Ly6C cells in lymph nodes, Thy1 T cells in skin versus \u003cem\u003eB. bacterium M13 \u003c/em\u003e\u003cstrong\u003eG. \u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eThy1 T cells and monocytes in skin versus \u003cem\u003eB. acidifaciens.\u003c/em\u003e \u003cstrong\u003eG.\u003c/strong\u003e CD45 and PMNs in skin versus Flavonifractor. Pearson R and P values significant as indicated, n = 9 irradiated littermates and n = 9 irradiated CLE mice pooled from n = 3 experiments.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7023225/v1/990e6f7674196af5598be7cd.png"},{"id":89351033,"identity":"44acdd02-8262-4158-b0f3-1b069cf704a5","added_by":"auto","created_at":"2025-08-19 06:10:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":115289,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional data in gut samples shows enhancement of integral membrane protein functional pathways in CLE mice. A. \u003c/strong\u003eTop 10 GO Term functional parameters in gut samples in copies per million. \u003cstrong\u003eB. \u003c/strong\u003eTop 10 KO Term functional parameters in gut samples in copies per million. Pre-induction mice pooled from n= 2 experiments, n = 4 pre-induction littermates and n = 6 pre-induction CLE mice. Post-induction littermates pooled from n = 3 experiments, n = 9 post induction littermates and n = 9 post-induction CLE mice.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7023225/v1/6022435d0e0f2b9fd6a65dd2.png"},{"id":89351042,"identity":"989d2091-c4dc-4a5f-b741-6f586f16c638","added_by":"auto","created_at":"2025-08-19 06:10:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":344384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProportional abundance of specific gut mycobiome strains differ by disease group. A. \u003c/strong\u003e\u003cem\u003eColletotrichum tofieldiae \u003c/em\u003eby experimental group.\u003cstrong\u003e B. \u0026nbsp;\u003c/strong\u003e\u003cem\u003eVerticillium\u003c/em\u003e by experimental group\u003cstrong\u003e. C. \u003c/strong\u003e\u003cem\u003ePeriglandula ipomoeae \u003c/em\u003eby experimental group\u003cstrong\u003e. D. \u003c/strong\u003e\u003cem\u003ePenicillium paneum\u003c/em\u003e by experimental group. Pre-induction mice pooled from n= 2 experiments, n = 4 pre-induction littermates and n = 6 pre-induction CLE mice. Post-induction littermates pooled from n = 3 experiments, n = 9 post induction littermates and n = 9 post-induction CLE mice.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7023225/v1/631ff282580c93c53e592901.png"},{"id":100614516,"identity":"f19dd9e3-80c9-4a59-80d1-6e928137a7b1","added_by":"auto","created_at":"2026-01-19 17:21:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2302954,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7023225/v1/e2480ecd-6db3-44bd-b961-269ee502f281.pdf"},{"id":89351212,"identity":"8f01411f-cc48-43e2-92dd-bd0ea9909847","added_by":"auto","created_at":"2025-08-19 06:18:58","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":450737,"visible":true,"origin":"","legend":"","description":"","filename":"Neffetallsupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-7023225/v1/ec5359a862aeeb3435d8affa.docx"},{"id":89351991,"identity":"39ae07c8-4793-4d78-8d6a-207121a7fffc","added_by":"auto","created_at":"2025-08-19 06:26:58","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7745,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1Immunedatainirradiatedmice.csv","url":"https://assets-eu.researchsquare.com/files/rs-7023225/v1/9726cf53ab1f8bff94604b75.csv"}],"financialInterests":"Competing interest reported. JMR is an inventor on patent application #62489191 and #15/851,651 which covers IL-15 and CXCR3 for the treatment of vitiligo, respectively; and on patent #63/478,900 filed for “Diagnosis of skin diseases in veterinary and human patients” for CTCL. The other authors have no conflicts of interest to disclose.","formattedTitle":"Gut Dysbiosis in a Murine Model of Cutaneous Lupus Erythematosus Correlates with Infiltration of Antigen-Specific T cells and Antigen Presenting Cells in Skin","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLupus is a complex and multifactorial disease process attributed to aberrant immune activation against the self, resulting in a heteromorphic symptomatology and several subtypes with varying organ involvement. Most lupus patients present with autoantibodies produced by B cells, autoreactive T cell subsets, and abnormally elevated levels of inflammatory cytokines, hallmarks of both innate and adaptive immune dysregulation \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Lupus impacting mucosal tissue or cutaneous tissue is termed cutaneous lupus erythematosus (CLE) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, which has an incidence of 4.2\u0026ndash;4.3 per 100,000 in the US \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. CLE and SLE are interrelated diseases: CLE occurs in as many as 80% of SLE patients, and patients that present with isolated CLE can progress to SLE. Adequate control of CLE mitigates the risk of systemic progression \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, whereas skin damage induced by UV light can exacerbate lupus nephritis \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe microbiome is a relatively new area of investigation for targeted therapy in lupus and other autoimmune diseases \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Gut microbiota have been heavily implicated in modulating activities of the immune system, including both innate and adaptive immune system activation and regulation \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Microbiota also play a role in the regulation of T cell maturation, and especially the activity of T regulatory cells, which are notably awry in autoimmune diseases \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDysbiosis, which is defined by a loss of beneficial microbial populations, an increase in deleterious bacterial species, and/or an inappropriately low microbial community diversity, can impair gut and skin barrier function, increasing permeability and thus exposure to the external environment. Dysbiosis has been reported in both lupus-prone mice and human systemic lupus patients \u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Dysbiosis can also trigger autoimmunity directly through molecular mimicry. In the case of lupus, Ro60 orthologues trigger onset of lupus in mice \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Few studies have been performed specifically on CLE, though \u003cem\u003eS. aureus\u003c/em\u003e outgrowth has been characterized in lesional skin\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. A subsequent study demonstrated that skin \u003cem\u003eS. aureus\u003c/em\u003e colonization could contribute to SLE, increasing glomerulonephritis and autoantibody deposition, in genetically-prone animals \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHere, we sought to characterize the gut microbiome in our novel B6 mouse model of CLE\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This model employs a TLR9KO lupus-prone background, with a keratin 5-driven ovalbumin under the control of a tetracycline response element. This genetic system is inducible and allows for flares. When these recipient mice are injected with Th2-skewed antigen-specific OT2 T cells, mice develop skin lesions that are clinically and histologically similar to human lupus, as well as splenomegaly and positive ANA\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. We used whole genome shotgun sequencing to analyze fecal pellets from these animals, identifying several bacterial and fungal species that could be targeted for future lupus therapeutic development.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eThe gut microbiome in CLE mice has a lower alpha diversity, and disease induction induces a loss of Prevotella and an outgrowth of Duncaniella\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we compared fecal microbiome samples from CLE and littermate control mice, both pre- and post-disease induction (\u003cstrong\u003eFig 1A\u003c/strong\u003e). Sex and cage number did not significantly cluster on PCA (\u003cstrong\u003eFig. 1B-C\u003c/strong\u003e), therefore samples were pooled for analysis by time (pre- versus post-induction) and genotype status (littermates versus CLE). Based on the weighted unique fraction (unifrac) distance matrix, which takes into account phylogenetic relatedness as well as relative abundance, many pre-induction \u0026nbsp;littermates and pre-induction CLE-prone mice were most similar to each other, though not all post-induction CLE mice were most similar to other post-induction CLE mice, occasionally seeming more similar to their post-induction littermates (\u003cstrong\u003eFig. 1D\u003c/strong\u003e). \u003cem\u003eFirmicutes/Bacteroidetes\u003c/em\u003e (F/B) ratios did not significantly differ amongst littermates and their CLE counterparts, though there was a trend towards increased Bacillota and reduced Bacteroidota (formerly Firmicutes) in CLE mice (\u003cstrong\u003eFig. 1E-F\u003c/strong\u003e). We also observed proportional changes in abundance of specific taxa in irradiated CLE compared to irradiated littermate mice (\u003cstrong\u003eFig S1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eNext, we assessed grouped analyses for pre- and post-induction CLE mice and littermates. Mice with more severe skin lesions tended to have fewer \u003cem\u003ePrevotella\u003c/em\u003e at the species and genus level (\u003cstrong\u003eFig. S2\u003c/strong\u003e). CLE mice also had the lowest alpha diversity (\u003cstrong\u003eFig 2C\u003c/strong\u003e) and trended towards lower beta diversity using Shannon\u0026rsquo;s Index but not Simpson\u0026rsquo;s (\u003cstrong\u003eFig 2D-E\u003c/strong\u003e). Examination of differences in pre- versus post-induction revealed a significant increase in \u003cem\u003eDuncaniella\u003c/em\u003e, \u003cem\u003eBacteroidales\u003c/em\u003e and\u003cem\u003e Lepagella\u003c/em\u003e species, (\u003cstrong\u003eFig 2A-B, 3D, S3\u003c/strong\u003e)and a decrease in \u003cem\u003ePrevotella \u003c/em\u003e(\u003cstrong\u003eFig 2A-B\u003c/strong\u003e)\u003cstrong\u003e. \u003c/strong\u003eTaken together, these data demonstrate that mouse genotype and disease induction process (irradiation, doxycycline administration, T cell injection) influence the gut microbiome in the CLE mouse model, and that diseased animals have a lower alpha diversity.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP. sartorii is significantly increased in CLE mice compared to littermates, and Bacteroides, Parabacteroides, Duncaniella and Blautia increase with disease whereas Alistipes decreases\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe next asked whether we could identify specific strains that change with disease status, with the goal of identifying potential treatment targets or probiotics. Examining the top 10 species by disease group identified stepwise increases in \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eParabacteroides\u003c/em\u003e and \u003cem\u003eDuncaniella\u003c/em\u003e, with the lowest abundance in pre-induction littermates and the highest abundance in post-induction CLE mice (\u003cstrong\u003eFig 3A-D\u003c/strong\u003e). Examining the top 10 strains by disease group identified a similar stepwise increase in \u003cem\u003eBlautia pseudococcoides\u003c/em\u003e, and a stepwise decrease in \u003cem\u003eAlistipes MGBC116833 \u003c/em\u003e(\u003cstrong\u003eFig 3E-G\u003c/strong\u003e). \u003cem\u003ePhocaeicola sartorii \u003c/em\u003ewas one of the few strains that was significantly increased in post-induction CLE versus littermate mice (\u003cstrong\u003eFig 3H\u003c/strong\u003e). Taken together, these data identify specific bacterial strains that might be useful targets for development of probiotics or strain-specific lysis such as through bacteriophage therapy.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSpecific strains correlate with immune infiltrates in skin as well as abundance in lymphoid organs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo understand the potential impact the gut microbiome could have on skin disease in our lupus model, we examined immune data across disease cohorts and correlated these data with microbiome data. We gated immune cells in the lymph node (LN), spleen (SP) and skin (SK) in post-induction littermates and CLE mice to identify which populations were significantly different in diseased animals (\u003cstrong\u003eFig 4A, Table S1\u003c/strong\u003e). Next, we correlated these significantly different immune cell populations with the top microbiome genera, namely host T cells in the lymph node, mouse weight, host T cells in the spleen, CD45 cells in the spleen, B cells in the spleen, spleen weight (splenomegaly), F4-80 Ly6Clo monocytes/macrophages in the lymph node, skin score, CD45 cells in the skin, F4-80 macrophages in the lymph node, and antigen specific T cells in the lymph node and skin (\u003cstrong\u003eFig 4B\u003c/strong\u003e). Heatmap matrix of bacteria genera and key immune data showed associations of higher \u003cem\u003eBacteroides, Parabacteroides, Duncaniella\u003c/em\u003e with higher skin scores, spleen weights, and specific cell lines related to disease, whereas \u003cem\u003eAkkermansia, Lawsonibacter, Lactobacillus, Schaedlerella\u003c/em\u003e, and \u003cem\u003eOdoribacter\u003c/em\u003e had negative associations with disease related flow data. Next, we examined strains that correlated with specific immune cell populations as identified in our correlation matrix, which we present in \u003cstrong\u003eFig 4C-H\u003c/strong\u003e. Specifically, we found positive correlations between \u003cem\u003eParabacterodies distasonis\u003c/em\u003e and \u003cem\u003eDuncaniella muris\u003c/em\u003e with antigen-specific T cells in the LN and SK; \u003cem\u003ePhocaeicola sartorii\u003c/em\u003e with F4-80 Ly6Clo monocytes/macrophages in the LN and monocytes in the SK; \u003cem\u003eBacteroidales bacterium M13\u003c/em\u003e with antigen-specific T cells in the SK and F4-80 Ly6Clo monocytes/macrophages in LN; \u003cem\u003eBacteroides acidifaciens \u003c/em\u003ewith antigen-specific T cells and monocytes in SK; and \u003cem\u003eFlavonifractor\u003c/em\u003e with total CD45+ and neutrophils (PMN) in SK.\u003c/p\u003e\n\u003cp\u003eWe also performed functional gene analyses on the bacterial strains and noted an increase in the term \u0026ldquo;integral membrane protein functional pathways\u0026rdquo; in CLE post-induction mice (\u003cstrong\u003eFig 5\u003c/strong\u003e). We hypothesize that this may relate to immune cell activation, as synthesis of bacterial membrane proteins, including upregulation of receptors, transport and membrane binding components, contribute to immune cell activation and extravasation to skin.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLoss of specific fungal strains is associated with CLE disease\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe also compared fungi abundance across cohorts, and expressed reads as percentages of overall number of classified reads for respective subjects. Fungi read counts were overall low in gut samples (\u0026lt;100); therefore, the data is of limited internal validity and was insufficient to calculate abundance by OneCodex. Overall trends showed lower proportions of \u003cem\u003eColletotrichum tofieldiae\u003c/em\u003e (p\u0026lt;0.05) and Verticillium in CLE gut microbiome compared to littermates, and an increased proportions of \u003cem\u003ePeriglandula ipomoeae\u003c/em\u003e and \u003cem\u003ePenicillium paneum\u003c/em\u003e in CLE compared to littermates, though these changes were similar to differences in pre-irradiation CLE mice versus littermates (\u003cstrong\u003eFig. 6\u003c/strong\u003e). Taken together, these data demonstrate the effect that genotype and disease induction (irradiation, doxycycline administration, T cell injection) has on the gut mycobiome.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCurrent first line treatment SLE or CLE is largely nonspecific immune suppression, increasing risk of infections, cancers, and other short- and long-term side effects. While changes in the microbiome have been investigated in both murine models and humans with SLE, limited research has focused on potential probiotic interventions and the impact of fungal communities on lupus. Significant and consistent microbiome differences across lupus mouse models and more recent human studies support a promising role for dietary interventions that increase microbial diversity and decrease inflammation \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Thus, the characterization of the microbiome and mycobiome can set the groundwork for further mechanistic and interventional study.\u003c/p\u003e\u003cp\u003eWe found no significant differences in F/B ratios from our mice, though normalized read counts of the top 10 phyla demonstrated an increase in Bacillota and a decrease in Bacteroidota in CLE mice post-induction. Interestingly, these findings are consistent with other SLE murine models in which there was no significant difference in F/B ratios between healthy and control mice, despite there being notable changes in the gut microbiome associated with disease progression and remission \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Literature suggests that F/B ratios are lower in mice than in humans and caution is needed when generalizing F/B ratios in model organisms as they may not reliably recapitulate the human gut microbiome \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Though alpha diversity trended lower, beta diversity by Simpson index was similar across cohorts. Notably, Shannon beta diversity, which gives more weight to rare species, was more different between CLE mice and littermates, indicating greater reductions in more rare species in diseased mice.\u003c/p\u003e\u003cp\u003eThe model induction process, which involves administration of doxycycline chow to turn on the model autoantigen and 400R irradiation to make room for antigen-specific T cells, impacts the microbiome in both CLE mice and littermate controls. Specifically, we noted a significant loss of \u003cem\u003ePrevotella\u003c/em\u003e, which is also observed to be lost in human gut microbiomes in response to Western diet \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003ePrevotella copri\u003c/em\u003e was recently reported to be lost in Korean SLE patient gut microbiomes \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003ePrevotella\u003c/em\u003e is sensitive to doxycycline, though it is unclear whether the dose administered to the mice (20mg/kg ad libitum) was responsible for the observed loss as long-term doxycycline administration at 20mg sub-antimicrobial doses has been reported to not have significant effects on gut or vaginal microbiota in humans \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Low dose irradiation was reported to reduce alpha diversity in mice, as well as specific metabolites \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. It would be interesting to study whether the specific loss of \u003cem\u003ePrevotella\u003c/em\u003e and reduced alpha diversity is sufficient to allow for the development of cutaneous lupus in mice as a result of the disease induction protocol.\u003c/p\u003e\u003cp\u003eWe found significant increases in \u003cem\u003eBacteroides, Parabaceteroides, Duncaniella, Blautia pseudococcoides\u003c/em\u003e and \u003cem\u003ePhocaeicola sartorii\u003c/em\u003e in our post-induction CLE mice compared to other groups, indicating that these strains might be associated with lupus inflammation. \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eParabactererodies\u003c/em\u003e species are increased in human lupus gut microbiomes, specifically in glucocorticoid negative patients \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This fits with our mouse model, as we did not provide glucocorticoids to the mice. \u003cem\u003eDuncaniella\u003c/em\u003e was increased in a study that performed fecal transplantation through feeding of WT or control (non-diseased) mouse fecal pellets to SLE-prone mice \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. It is possible that our mice had increases in \u003cem\u003eDuncaniella\u003c/em\u003e through coprophagy from littermates that were housed together in their cages. However, it is unclear why they would have higher levels compared to littermates, unless there is an effect of genotype and/or inflammatory status on abundance of this phylum. A recent study characterizing microbiome changes in lupus patients identified \u003cem\u003eBlautia gnavus\u003c/em\u003e blooms during flares \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eB. gnavus\u003c/em\u003e used to be considered a part of the \u003cem\u003eB. pseudococcoides\u003c/em\u003e family but was recently reclassified \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. It would be interesting to further investigate whether or not these strains perform homologous functions in the context of lupus across species. \u003cem\u003eP. sartorii\u003c/em\u003e was recently reported to be enriched in the gut of mild, but not severe, lupus in MRL/lpr mice \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Given that our model primarily impacts the skin, with mild if any kidney disease, this fits with this observation.\u003c/p\u003e\u003cp\u003eAntigen-specific T cell infiltration into the skin was positively correlated with \u003cem\u003eParabacteroides distasonis, Duncaniella muris, Bacterodiales bacterium M13\u003c/em\u003e and \u003cem\u003eBacteroides acidifaciens\u003c/em\u003e. \u003cem\u003eP. distasonis\u003c/em\u003e was recently reported to promote CXCL9 secretion by tumor-associated macrophages which in turn promoted CD8 + T cell activation and anti-tumor immunity in the context of lung cancer \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. How this activity could influence skin infiltration of T cells in an autoimmune setting requires further investigation. \u003cem\u003eD. muris\u003c/em\u003e was recently described to have different clinical isolates \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, one of which is associated with anti-inflammatory properties in a DSS colitis model \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. It is unclear whether the isolate in our mice is pro- or anti-inflammatory, though our data suggests that it might act in a pro-inflammatory capacity in the TLR9KO genetic background, given the increase in antigen-specific T cells in the skin. Antigen presenting cell populations’ infiltration into the skin, including monocytes (Ly6C + Ly6G-) and F4-80 + Ly6Clo macrophages, were positively correlated with \u003cem\u003eP. sartorii\u003c/em\u003e and \u003cem\u003eB. acidifaciens\u003c/em\u003e, with higher lymph node numbers positively correlated with \u003cem\u003eP. sartorii\u003c/em\u003e and \u003cem\u003eB. bacterium M13\u003c/em\u003e. How \u003cem\u003eP. sartorii\u003c/em\u003e or \u003cem\u003eB. bacterium M13\u003c/em\u003e impact monocyte and macrophage populations remain unclear. \u003cem\u003eB. acidifaciens\u003c/em\u003e is associated with inflammation-induced tumorigenesis in DSS models, though the effect it might exert on tumor-associated macrophages is unclear \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Last, neutrophils and total CD45 + inflammatory cells were positively correlated with \u003cem\u003eFlavonifractor\u003c/em\u003e. \u003cem\u003eFlavonifractor\u003c/em\u003e is increased in the gut of bullous pemphigoid patients, providing a gut-skin connection with relevance to a skin disease that is also characterized by neutrophilic infiltration \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. It would be interesting to test targeting of each of these strains in CLE to determine the impact on skin inflammation and clinical disease scores. This could be achieved through bacteriophage therapy, which is currently being investigated primarily for infectious diseases \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. One specific example of this is a preclinical phage therapy that improved immunity to \u003cem\u003eS. aureus\u003c/em\u003e in immunocompromised mice \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Given that \u003cem\u003eS. aureus\u003c/em\u003e is enriched in CLE skin\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, this could be a promising therapeutic for lupus skin disease.\u003c/p\u003e\u003cp\u003eMicrobiota have been investigated to impact metabolism through augmentation of human metabolic pathways, a well-known symbiotic relationship in which bacteria supply enzymatic capabilities its host would otherwise not have \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Functional data in our model showed an upregulation of membrane proteins, which includes transporter families and cellular attachment and signaling proteins such as lectins and binding receptors. All of these ligands have the potential to stimulate innate immune receptors, which could contribute to lupus immunopathogenesis \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eStudies have also demonstrated changes in the gut’s fungal populations, or the mycobiome, in SLE \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. One pilot study found human SLE mycobiomes demonstrated distinct dysbiosis compared to healthy controls and rheumatoid arthritis \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Another study reported different mycobiome beta diversities in three cohorts of SLE with lupus nephritis (LN), SLE without LN, and healthy controls, with both SLE cohorts having increased ratio of opportunistic fungi and Aspergillus being correlated with 24 hour proteinuria, anti-dsDNA and ANA \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Murine lupus models including FcGRIIb deficient mice and pristane treated mice exhibit an elevated Basidiomycota-to-Ascomycota ratio that was positively correlated with disease severity \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In our model, the fungal strain \u003cem\u003ePenicillium paneum\u003c/em\u003e was higher in CLE mice versus littermates, while \u003cem\u003eColletotrichum tofieldiae\u003c/em\u003e was absent. Although low fungal read yields limit interpretation of this data, we hypothesize that fungal probiotics that are lacking in CLE post-induction mice could be further tested for utility as probiotic strains. \u003cem\u003eColletotrichum tofieldiae\u003c/em\u003e is a particularly interesting candidate given it is considered a beneficial root probiotic \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and might be enriched in whole food diets \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLimitations of our study include small sample size which limited power to detect significant differences. Mice were selected from several experiments that occurred at separate times, in a clean but not germ-free environment. Intermouse grooming and coprophagia may introduce microbiome congruence across disease cohorts. Males and females were analyzed together based on our PCA analysis, though sex hormones have been shown to play a role in microbiome composition in other murine models. Last, while analysis of the gut microbiome in murine models is useful for hypothesis generation and particularly for interventional studies involving the microbiome, the mouse microbiome cannot be directly extrapolated to humans.\u003c/p\u003e\u003cp\u003eIn conclusion, we present this paper as a characterization of a CLE mouse model’s gut microbiome for future interventional study, as well as for hypothesis generation in further study in humans. Future directions include investigation of the skin microbiome in this model, as well as mechanistic studies understanding how specific strains and populations impact disease progression are planned.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Materials \u0026 Methods","content":"\u003cp\u003e\u003cstrong\u003eMouse model \u0026amp; fecal pellet sampling\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e Animal protocol was reviewed and approved by the UMass Chan IACUC (#202100229), and studies were conducted in accordance with the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals. Eighteen (18) mice were selected for analysis from a representative cohort of CLE mice and littermate controls from 2–3 separate experiments. Disease was induced as previously described\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e using 10\u0026nbsp;million Th2 skewed OT2 CD4 + T cells adoptively transferred into sublethally irradiated recipient mice (400R). Mice also received 200mg/kg doxycycline chow (BioServ) ad libitium to turn on the ovalbumin model autoantigen in keratin 5 expressing cells (keratinocytes). Severity of skin lesions were collected weekly on a 0–4 scale, with 0 indicating no skin changes and 4 indicating at least 75% skin involvement. Stool samples from pre- (week 0) and post-induction (week 3–4) were snap frozen at -70C and batched for analysis. We confirm the study design is reported in accordance with ARRIVE guidelines.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMicrobiome sample preparation\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eTransnetyx Microbiome kits containing barcoded sample collection tubes were provided by Transnetyx (Cordova, TN, USA). Mouse fecal samples were placed in individual tubes containing DNA stabilization buffer to ensure reproducibility, stability, and traceability, and shipped for DNA extraction, library preparation, and sequencing by Transnetyx (Cordova, TN USA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDNA Extraction and Metagenomic Sequencing\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eDNA extraction was optimized and fully automated using a robust process for reproducible extraction of inhibitor-free, high molecular weight genomic DNA that captures the true microbial diversity of stool samples. After DNA extraction and quality control (QC), genomic DNA was converted into sequencing libraries using a method optimized for minimal bias. Unique dual indexed (UDI) adapters were used to ensure that reads and/or organisms are not mis-assigned. After QC, the libraries were sequenced using the shotgun sequencing method (a depth of 2\u0026nbsp;million 2x150 bp read pairs), which enables species and strain level taxonomic resolution. Sequencing data were uploaded automatically onto One Codex analysis software and aligned against the One Codex database consisting of ~ 148K complete microbial genomes, including 71K distinct bacterial genomes, 72K viral genomes, and thousands of archaeal and eukaryotic genomes. The classification results were filtered through several statistical post-processing steps designed to eliminate false positive results caused by contamination or sequencing artifacts. Samples were compared with TransnetYX’s global diversity averages from historic data for quality control check. Low readcounts were proportionalized to the total number of identifiable reads from their host sample. Sequencing data was aligned with the Gene Ontology (GO) and KEGG Orthology databases for functional analysis on the OneCodex platform.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOneCodex python package, Jupiter notebooks, and GraphPad Prism were used for analysis. All data are displayed as mean ± SD. Multivariate analyses of skin score versus relative abundance of bacteria were repeated at different taxonomic levels using GraphPad Prism. Taxa with significant p-value correlation to skin score were extracted to compare across cohorts. Gaussian distribution was verified using the Anderson-Darling test. One-way ANOVAs with posttests were performed to compare all 4 groups (littermates pre-induction, littermates post-induction, CLE pre-induction, CLE post-induction), and normality tests and tests for standard deviation variations were conducted to ensure the correct type of ANOVAs and post-tests were selected. We also employed an unpaired t-test with Welch’s correction to compare between littermates and CLE mice post-disease induction when data were normally distributed. Mann-Whitney test was used when data were non-normally distributed. Network analyses are constructed by OneCodex package on Jupytr and Morpheus.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: Supported by a Lupus Research Alliance Mechanisms \u0026amp; Targets Award (JMR), UMass Chan startup funds (JMR) and UMass Chan Research \u0026amp; Curriculum Exploration summer stipend (HN).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: We thank Dr. Maldonado-Contreras and Dr. McCormick for their advice, expertise and enthusiasm in microbiome research. We also thank Denise and Austin at OneCodex for helping with Python, and Samira at TransNetYX for all her guidance with sample preparation and analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e: JMR is an inventor on patent application #62489191 and #15/851,651 which covers IL-15 and CXCR3 for the treatment of vitiligo, respectively; and on patent #63/478,900 filed for \u0026ldquo;Diagnosis of skin diseases in veterinary and human patients\u0026rdquo; for CTCL. The other authors have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e The datasets generated and/or analysed during the current study are available in the OneCodex repository, available via the following links:\u003c/p\u003e\n\u003cp\u003eGut microbiome project: https://app.onecodex.com/projects/120f7b4173934b33\u003c/p\u003e\n\u003cp\u003eUnifrac and heatmaps Jupytr notebook: https://app.onecodex.com/notebooks/public/160b861a6f8d4910\u003c/p\u003e\n\u003cp\u003ePCAs and alpha/beta diversity Jupytr notebook: https://app.onecodex.com/notebooks/public/fc7ddac768c645a2 .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCREdiT statement\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eConceptualization: JMR, HN\u003c/p\u003e\n\u003cp\u003eMethodology: JMR\u003c/p\u003e\n\u003cp\u003eSoftware: N/A\u003c/p\u003e\n\u003cp\u003eValidation: DW, DS, ZGRO\u003c/p\u003e\n\u003cp\u003eFormal analysis: HN, JMR, NS\u003c/p\u003e\n\u003cp\u003eInvestigation: UYA, HN\u003c/p\u003e\n\u003cp\u003eResources: JMR\u003c/p\u003e\n\u003cp\u003eData curation: HN, JMR\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: HN, JMR\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review \u0026amp; editing: all authors\u003c/p\u003e\n\u003cp\u003eVisualization: HN\u003c/p\u003e\n\u003cp\u003eSupervision: JMR\u003c/p\u003e\n\u003cp\u003eProject administration: JMR\u003c/p\u003e\n\u003cp\u003eFunding acquisition: JMR, HN\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGuo, X. et al. 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Rep.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 24017 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"lupus, microbiome, mycobiome, autoimmune, shotgun sequencing","lastPublishedDoi":"10.21203/rs.3.rs-7023225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7023225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe commensal organisms constituting the human microbiome are increasingly appreciated to fortify epithelial barriers and modulate host immunity. Dysbiosis of both single strains and communities can contribute to inflammatory conditions. Here, we sought to characterize potential dysbiosis in our inducible mouse model of cutaneous lupus erythematosus (CLE). We hypothesized that gut dysbiosis would occur based on several studies that found lower \u003cem\u003eFirmicutes/Bacteroidetes\u003c/em\u003e (F/B) ratios and decreased diversity in systemic lupus erythematosus (SLE) cohorts compared to healthy counterparts, a mouse study that identified Ro60 commensal orthologs that can trigger onset of lupus-like disease, and a study of CLE that identified outgrowth of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e in the skin. Using whole genome shotgun sequencing, we identified differences in pre- and post-irradiation cohorts, particularly an increase in \u003cem\u003eDuncaniella\u003c/em\u003e, a decrease in \u003cem\u003ePrevotella\u003c/em\u003e, and a reduction in alpha diversity following irradiation. Baseline alterations in CLE mice gut bacteria compared to littermate controls were also extant, including trends toward increased \u003cem\u003eParabacterides distasonis\u003c/em\u003e and \u003cem\u003eBacteroides acidifaciens\u003c/em\u003e in CLE mice. Importantly, we noted an increase in \u003cem\u003ePhocaeicola sartorii\u003c/em\u003e in CLE mice compared to littermate controls post-disease induction. We examined the mycobiome in our mice and noted a reduction of \u003cem\u003eColletotrichum tofieldiae\u003c/em\u003e specifically in CLE mice post-disease induction, and a trend towards increased \u003cem\u003ePeriglandula ipomoeae\u003c/em\u003e. Last, we correlated abundance of genera and species with flow cytometry data obtained from the skin, lymph node and spleen, and identified specific strains that correlated with presence of antigen-specific T cells and different antigen presenting cell populations. Thus, our model exhibits similar changes to other models of lupus-like disease, and our data identify potential novel strains/species that could be modified for CLE and/or SLE treatment such as through generation of probiotics or specific antimicrobial agents.\u003c/p\u003e","manuscriptTitle":"Gut Dysbiosis in a Murine Model of Cutaneous Lupus Erythematosus Correlates with Infiltration of Antigen-Specific T cells and Antigen Presenting Cells in Skin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 06:10:54","doi":"10.21203/rs.3.rs-7023225/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-18T21:32:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T03:48:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-17T21:24:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-14T02:27:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-07T09:12:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273916341459780398530328049522563255864","date":"2025-08-23T19:14:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207662671541374137637102019348520189436","date":"2025-08-22T13:23:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51976775408807441021982875753396853695","date":"2025-08-19T11:36:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229230169536573905499878186417267447738","date":"2025-08-17T11:58:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1363179747741310813035661371577297704","date":"2025-08-17T10:59:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-17T10:26:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T20:40:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-18T13:36:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-18T13:30:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d76ded8-3b2f-4ca3-9445-0affe0a40298","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53319218,"name":"Biological sciences/Immunology"},{"id":53319219,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-01-19T16:45:38+00:00","versionOfRecord":{"articleIdentity":"rs-7023225","link":"https://doi.org/10.1038/s41598-025-34741-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-12 16:28:46","publishedOnDateReadable":"January 12th, 2026"},"versionCreatedAt":"2025-08-19 06:10:54","video":"","vorDoi":"10.1038/s41598-025-34741-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-34741-6","workflowStages":[]},"version":"v1","identity":"rs-7023225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7023225","identity":"rs-7023225","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}