Eubiotic gut microbiota mitigate colitis via colonic Treg expansion.

Eubiotic gut microbiota mitigate colitis via colonic Treg expansion. | 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 Eubiotic gut microbiota mitigate colitis via colonic Treg expansion. Eri Ikeda, Nobuyuki Okahashi, Shigetada Kawabata This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6225383/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 Aberrant gut microbiota has been linked to a disrupted immune response, leading to intestinal pathological conditions 1 , 2 . However, the causality and mechanisms have remained unclear. Here we used a wild-type mouse substrain resistant to dextran sulfate sodium (DSS)-induced colitis to understand the characteristics of the eubiotic microbiota and the mechanisms underlying colitis resistance. Colitis-resistant mice presented a significantly greater colonic Foxp3 + regulatory T (Treg) population after DSS challenge, while numbers of helper T1 and 17 cells were comparable. Cohousing experiments revealed that Treg expansion was induced by the gut microbiota of colitis-resistant mice, and mice with eubiotic microbiota maintained high concentrations of three microbial metabolites, SCFAs (acetate, butyrate, and propionate). Furthermore, proteomics revealed that iron binding and iron-metabolizing ceruloplasmin silencing were upregulated relating disease-resistance. Of note, iron plays a pivotal role to SCFA-producing bacteria. Taken together, our results reveal that silencing ceruloplasmin expression restricts iron metabolism, thereby increasing SCFA production. The eubiotic gut microbiota and combinations of SCFAs contribute to Treg induction, which helps control colitis progression. Health sciences/Gastroenterology/Gastrointestinal diseases/Inflammatory bowel disease/Ulcerative colitis Health sciences/Gastroenterology/Gastrointestinal models Health sciences/Gastroenterology/Gastrointestinal diseases/Dysbiosis Health sciences/Pathogenesis/Immunopathogenesis/Adaptive immunity/Immune tolerance/Peripheral tolerance ceruloplasmin inflammatory bowel disease regulatory T-cells short-chain fatty acids ulcerative colitis Figures Figure 1 Figure 2 Figure 3 Figure 4 Main text The gastrointestinal tract is an organ that is in close proximity to and is continually exposed to the gut microbiota 3 , 4 . The gut microbiota can ferment host-indigestible dietary fibers and carbohydrate polymers, thereby producing beneficial metabolites that are indispensable for the survival of the mammalian host 5 . Consequently, humans have coevolved with the gut microbiota for millions of years. This symbiotic relationship demonstrates the deep biological integration between humans and the microbiota and is considered a “superorganism” 6 . Among bacterial fermentation products, short-chain fatty acids (SCFAs) containing 1–6 carbons play pivotal roles in shaping intestinal health. SCFAs fortify barrier function by promoting the production of antimicrobial agents and retaining epithelial barrier integrity; acetate, butyrate, and propionate are the most prominent of these 7 , 8 . In addition, increasing evidence suggests that SCFAs regulate mucosal and systemic immunity 9 . Inflammatory bowel disease (IBD), chronic inflammatory diseases affecting the intestine, consists mainly of ulcerative colitis and Crohn’s disease; its incidence is increasing worldwide 10 . IBD is a multifactorial disorder involving immune, genetic, and environmental factors, including dysbiosis of the gut microbiota. In addition to compositional microbial changes, functional changes that reduce the fecal concentrations of SCFAs have also been reported 11 , 12 . Atypical innate and adaptive immune responses to microbial antigens have widely been acknowledged. Notably, an imbalance of two novel subsets of lymphocytes, helper T (Th) cells and regulatory T (Treg) cells, contributes to colitis. Overactivation of Th17 and/or Th1 cells results in intestinal inflammation. In contrast, Foxp3 + regulatory T (Treg) cells inhibit inappropriate induction of inflammatory T-cells, contributing to tuning immune tolerance 13 . Colonic Treg cells that reside in the lamina propria are distinct populations and are present at higher frequencies in the intestine than in other organs 14 . A lower abundance of colonic Treg cells in germ-free mice has been observed, and the abundance was increased by the administration of Clostridia species 15 , indicating that the gut microbiota and their metabolites promote the differentiation, accumulation, and function of Foxp3 + Treg cells 16 , 17 . Butyrate induces intestinal regulatory Treg cell differentiation through histone deacetylase (HDAC) inhibition at the Foxp3 promoter. Propionate, but not acetate, has also been reported to increase histone H3 acetylation, which enhances Treg cell induction 18 . In addition to SCFA-induced Treg cell differentiation via HDAC inhibition, the combined administration of acetate, propionate and butyrate stimulates the differentiation of colonic Treg cells and confers protection against colitis through a G protein-coupled receptor (GPR) 43-dependent mechanism 19 . We have previously identified a wild-type mouse substrain that is resistant to dextran sulfate sodium (DSS)-induced colitis, and the disease severity was determined mainly by the gut microbiota, including SCFA producers 20 . Altered gut microbiota composition and function are commonly observed in IBD patients 21 , but the significance of these changes remains unknown. To understand whether gut microbial dysbiosis is a main cause of disease onset in patients with colitis, we investigated the mechanism contributing to disease progression. Substrain differences in colonic Treg accumulation Because we demonstrated differences in the susceptibility of DSS-induced colitis among mice obtained from different vendors 20 , we postulated that intestinal immune cells may be associated with disease severity. We used BALB/C mice (6–7 weeks, female) purchased from two vendors: SLC and Charles River. Colitis was induced over 8 days by the administration of 4% (w/v) DSS in the drinking water (Fig. 1 A). The severity of DSS-induced colitis was investigated with respect to clinical parameters, weight loss, rectal bleeding, stool consistency and colon shortening. Consistent with our previous report, mice from SLC showed relatively fewer clinical symptoms, and mice from Charles River exhibited adequate colitis symptoms (Fig. 1 B and C). To define the contributions of the immune cells that induce disease susceptibility, proportions of CD4 + helper T-cells, known initiators and regulators of colitis in the colonic lamina propria, were compared in mice from the two vendors (Fig. 1 D and supplementary Fig. 1). Compared with Charles River mice (high susceptibility), SLC mice (low susceptibility) exhibited expansion of colonic Tregs after DSS induction (Fig. 1 E). In contrast, the proportions of Th1 or Th17 cells were not significantly different between the two groups (Fig. 1 F and G). To corroborate that Treg induction was due to differences in the gut microbiota between mouse substrains from different vendors, cohousing analysis was performed; this allows for the transfer of gut microbiota between mice. Low-susceptibility SLC mice were cohoused with high-susceptibility Charles River mice to allow horizontal bacterial transmission (Fig. 2 A). Cohoused SLC mice produced adequate colitis symptoms and that the severity of colitis in cohoused SLC mice was significantly greater than that in solo-housed SLC mice (Fig. 2 B). Cohoused SLC mice and solo-housed SLC mice are considered genetically identical. Consistently, compared with solo-housed SLC mice, cohoused SLC mice presented decreased Treg populations (Fig. 2 C and D). Thus, cohousing-mediated microbiota transfer outweighed the effects of colitis persistence along with Treg inhibition. Gut microbiome and these metabolites against colitis To determine the microbial changes induced by cohousing-mediated microbiota transfer, we explored the ability of the gut microbiota to reflect susceptibility to colitis. We previously reported that the gut bacterial composition of mice from SLC and Charles River were distinct even before DSS treatment, and Class Clostridia were significantly abundant in Charles River mice 20 . Reanalysis of our previously published microbiome dataset revealed that the composition of the gut microbiota in mice from disease-resistant solo-housed SLC mice and disease-susceptible Charles River and cohoused SLC mice analyzed using 16S rRNA sequences was comparable (Fig. 3 A and B). Short-chain fatty acids producing Turicibacter and Alistipes , were significantly abundant in disease-resistant SLC mice after DSS challenge. Figure 3 C shows the calculation of Bray-Curtis dissimilarity, a metric used to quantify the dissimilarity of the microbiota. Changes in microbiota between the pre- and post-DSS groups were not significantly different among the SLC, CO-SLC, and Charles River mice, suggesting that SLC mice changed their microbial composition because of DSS despite its disease resistance. The expansion of Tregs expressing Foxp3 suppresses inflammation in the colonic mucosa, thereby contributing to the attenuation of colitis. Short-chain fatty acids, microbial metabolites, are known as colonic Foxp3 + Treg inducers 19 . We obtained cecal contents from SLC and Charles River mice, and abundance of the main colonic SCFAs, acetate, propionate, and butyrate, was measured by GC/MS (Fig. 3 D-F). The concentrations of acetate (Fig. 3 D) and butyrate (Fig. 3 E) in pre-DSS mice were not significantly different between Charles River and SLC mice, but the concentration of propionate (Fig. 3 F) was significantly greater in SLC mice (p = 6.7×10 − 3 ). Consistent with previous findings 22 , 23 , DSS challenge caused a decrease in the levels of all three SCFAs, acetate, butyrate, and propionate, which were decreased in high-susceptibility Charles River mice (p = 1.9×10 − 6 , 2.7×10 − 4 , and 5.8×10 − 2 , respectively). Remarkably, low-susceptibility SLC mice maintained all their SCFA concentrations at a level equivalent to those of pre-DSS SLC and Charles River mice and significantly greater than those of colitic Charles River mice (p = 3.5×10 − 4 , 3.7×10 − 2 , and 2.3×10 − 3 , respectively). The disease resistance of SLC mice may result from the abundance of one or a combination of the three SCFAs. Colonic proteomics underlying the mechanisms of colitis resistance We next conducted DIA proteomics analysis to compare the landscape of host-derived colonic proteins between mouse strains with different susceptibilities from two different vendors. Colon tissues were collected from Charles River mice and SLC mice and analyzed via DIA proteomics. Among the 6981 host-derived proteins identified, 49 molecules were more abundant (upregulated) in low-susceptibility SLC mice, whereas 61 molecules were more abundant (downregulated) in high-susceptibility Charles River mice (greater than twofold, p < 0.05) (Fig. 4 A). Among these upregulated/downregulated proteins, the top five were selected based on their p value. The top proteins with the most highly upregulated (disease-ameliorate) expression were CBG, IRGM1, MSMO1, PAK1, RPL24, and ZHX2. Notably, PAK1 (P21-activated kinase 1) is a serine/threonine-protein kinase that regulates intestinal epithelial cell healing using SCFA 24 . Within the fluctuating proteins, we analyzed the biological pathways and functions that affect disease resistance. Among the enriched disease-resistant reactome pathways, L13a-mediated translational silencing of ceruloplasmin expression was upregulated (Fig. 4 B and Supplementary Fig. 3). Ceruloplasmin is an acute-phase protein that is responsible for copper transport and iron metabolism. Plasma iron homeostasis is maintained by strict regulation of gut iron absorption 25 . Iron ion binding was upregulated in the disease-ameliorating gene ontology (GO) terms (Fig. 4 C) and copper ion binding was upregulated in the disease-aggravating GO terms (Fig. 4 D). This is consistent with previous studies in which reduced iron absorption and increased serum copper concentration along with CRP have been shown in with the IBD patients 26 , 27 . Iron has proven to be indispensable for gut SCFA production 28 . Collectively, our results demonstrate that silencing ceruloplasmin expression restricts iron metabolism, thereby increasing SCFA production as well as preventing colitis progression. Discussion The underlying cause of IBD is challenging to determine, as it involves a combination of genetic predispositions, immune dysregulation, and environmental factors. Most epidemiological studies have focused on patients with active or treated disease, indicating that inflammation and tissue damage have already developed. This makes it difficult to determine whether the observed changes are attributable to the initiation and subsequent progression of disease. It is crucial to identify the factors that influence disease onset. We showed that in mice with eubiotic gut microbiota, which are thus less susceptible to DSS-induced colitis, the expansion of the colonic Treg population by the gut microbiota and microbial SCFA metabolites protect against colitis. In contrast, mice that have noneubiotic gut microbiota but are genetically identical to mice with lower susceptibility show lower Treg levels, thus leading to the development of colitis. In IBD patients, fecal microbiota transplantation (FMT) has shown potential as a therapeutic intervention 29 . FMT is a technique that aims to improve the diversity and abundance of the gut microbiota by transplanting healthy feces, which leads to the restoration of gut microbial eubiosis. Notably, the eubiotic microbiota plays a pivotal role in the onset and progression of colitis. SCFAs are prime examples of beneficial metabolites produced from microbiota-accessible carbohydrates. Among all SCFAs, butyrate is reported to be utilized the most efficiently 30 . We showed that the concentrations of acetate, butyrate, and propionate in the cecal contents are correlated with disease development. Bacterial cross-feeding influences the final balance of SCFA production and the effective utilization of substrates that reach the gut 31 , 32 . The butyrogenic effects of dietary fiber are the result of cross-feeding between butyrate-producing and nonbutyrate-producing strains. For example, butyrogenic bacteria produce butyrate by utilizing fermentation byproducts, such as acetate, generated during the initial breakdown of dietary carbohydrates. The production levels of acetic acid and propionic acid are also considered important. SCFAs promote intestinal epithelial cell wound healing through PAK1 and MFGE8 signaling 24 . PAK1 belongs to a serine‒threonine kinase family and contributes to the regulation of crypt homeostasis during intestinal inflammation by controlling Notch1 33 . PAK1 was one of the top 5 proteins related to disease resistance, as shown in Fig. 4 . Together, these findings suggest that SCFAs produced by gut bacteria suppress inflammation via the PAK1 pathway, enabling SLC mice to exhibit resistance to colitis. Our study identified that mice with eubiotic gut microbiota show lower susceptibility to DSS-induced colitis and expansion of the colonic Treg population via the gut microbiota and microbial SCFA metabolites. Silencing ceruloplasmin expression restricts iron metabolism, thereby increasing SCFA production. Resistance to colitis was induced in a genetics-independent manner. Therefore, on the basis of the effects of the gut microbiota and these metabolites on host immune responses, our results may reveal the importance of eubiotic gut microbiota in the prevention of colitis. Methods Mice BALB/c mice were obtained from SLC Japan (BALB/cCrSLC, Shizuoka, Japan) and Charles River Laboratories Japan (BALB/cAnNCrlCrlj, Kanagawa, Japan) and housed under SPF conditions. For the cohousing experiment, four-week-old female BALB/c mice purchased from SLC and Charles River were cohoused for 4 weeks as described previously 20 . Acute colitis was induced by the oral administration of 4% (w/v) DSS (MP Biomedicals, Santa Ana, CA, USA) in the drinking water for eight days per the experiment. The progression of colitis was assessed as previously described 20 . All animal experiments were approved by the Animal Care and Use Committee of Osaka University Graduate School of Dentistry (R05-009-0). We confirm that all the animal experiments were performed in accordance with the ARRIVE guidelines 2.0. Isolation of lymphocytes Lamina propria cells were isolated from the colons. The dissected colon tissues were treated with 1 mM EDTA at room temperature for 1 h to remove epithelial cells and then minced into Hanks’ balanced salt solution with collagenase type I (200 U/ml, Wako, Osaka, Japan) and DNase (20 U/ml, Worthington Biochemical, Lakewood, NJ, USA) at 37°C for 30 min. After filtering, lymphocytes were collected. Flow cytometry To analyze the percentage of Th cells in the colon, the lymphocytes isolated as described above were stained according to the manufacturer’s guidelines. For intercellular staining of cells, a mouse Treg cell staining kit (eBioscience, San Diego, CA, USA) was used. The following antibodies were obtained from Thermo Fisher Scientific (MA, USA): anti-CD45 (30-F11), anti-CD11b (M1/70), anti-CD3 (145-2C1), anti-CD4 (RM4-5), anti-Foxp3 (FJK-16S), anti-T-bet (4B10), and anti-Rorγt (B2D), and Fixable Viability Dye (Supplementary Fig. 1). Flow cytometer we used was BD FACS Canto II (NJ, USA). Short-chain fatty acid quantification Short-chain fatty acids in the cecal contents were derivatized by pentafluorobenzylation and quantified via gas chromatography‒mass spectrometry (GC‒MS) as previously described with minor modifications 34 . In brief, metabolites were extracted from the cecal contents and mixed with the deuterium-labeled internal standards acetate-d3 (Cambridge Isotope Laboratories, Tewksbury, MA, USA), butyrate-d7 (CDN isotopes, Quebec, Canada), and propionate-d5 (Cambridge Isotope Laboratories). The derivatized SCFAs were analyzed via a GC‒MS QP2020 (Shimadzu Corporation, Kyoto, Japan). Proteomics Intestinal proteins were extracted from the colon tissues soaked in lysate (2% sodium deoxycholate, 50 mM Tris, and 150 mM NaCl) containing a protease inhibitor cocktail (cOmplete, Sigma Aldrich, St. Louis, MO, USA), homogenized in a MagNA Lyser (Roche, Basal, Switzerland), and sonicated on ice for 60 min. The supernatants were quantified by a BCA Bradford assay (Bio-Rad, San Diego, CA, USA). The purified samples were analyzed by using UPLC (Vanquish Neo; Thermo Fisher Scientific) and MS (Orbitrap Exploris 480; Thermo Fisher Scientific). Peptides were identified via the data-independent acquisition (DIA) technique. The raw data were analyzed and visualized using Scaffold DIA (V 3.4.1). Upregulated and downregulated protein expression (greater than twofold, p < 0.05) were selected as inputs for functional analysis. Reactome pathway analysis was performed using g:Profiler (ve112_eg59_p19_25aa4782) and reactome pathway database (v91). GO enrichment analysis was performed using UniProt (v2024_06), and org.Mm.eg.db (v3.20.0). Converted genes were mapped in the and GO Resource (v40635). Reanalysis of fecal metagenome samples We reanalyzed the publicly available fecal metagenomes from our previous study, which were deposited in the DDBJ under accession number PRJDB15999. Statistics Comparisons of two groups were performed using an unpaired t (parametric) test or a Mann‒Whitney U (nonparametric) test. Differences among more than three groups were evaluated using one-way analysis of variance for parametric analysis or the Kruskal‒Wallis test for nonparametric analysis followed by Bonferroni correction (parametric) or Steel‒Dwass correction (nonparametric). The normality of the data was analyzed via the Kolmogorov‒Smirnov test. The homogeneity of variance was analyzed via the F test (two groups) or Bartlett test (more than three groups). The error bars represent the standard de viation of a dataset. All the statistical analyses were performed with EZR statistical software. Declarations Competing interests The authors declare that they have no competing financial interests. Additional information Supplementary Information is available for this paper. Correspondence and requests for materials should be addressed to Eri Ikeda. Peer review information Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Reprints and permissions information is available at www.nature.com/reprints . Author Contributions EI and SK designed experiments. EI performed animal experiments, immune analysis, proteomics, and metagenomics. NO analyzed the metabolites production of the colon. EI was a major contributor in writing the manuscript. All authors read and approved the final manuscript. Acknowledgments The flow cytometry analysis was supported by the Center for Medical Research and Education, Graduate School of Medicine, Osaka University. This work was also supported by the Osaka University program for the support of networking among present and future researchers. This work was financially supported by the Japan Society for the Promotion of Science, and the Futoku Foundation. Data availability The proteome data were deposited in the Japan Proteome Standard Repository under project no. JPST003521. References Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569 , 655-662, doi:10.1038/s41586-019-1237-9 (2019). Li, Y. et al. Identification of trypsin-degrading commensals in the large intestine. Nature 609 , 582-589, doi:10.1038/s41586-022-05181-3 (2022). Khalessi, A. et al. Differential Manifestations of Inflammatory Bowel Disease Based on Race and Immigration Status. Gastro Hep Adv 3 , 326-332, doi:10.1016/j.gastha.2023.11.021 (2024). Ikeda, E., Yamaguchi, M., Ono, M. & Kawabata, S. In vitro acid resistance of pathogenic Candida species in simulated gastric fluid. 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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-6225383","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":436874978,"identity":"db831d4b-f630-4405-8b34-6a4961a4f7c7","order_by":0,"name":"Eri Ikeda","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-2945-1089","institution":"Osaka University","correspondingAuthor":true,"prefix":"","firstName":"Eri","middleName":"","lastName":"Ikeda","suffix":""},{"id":436874979,"identity":"963f62e1-d165-4903-8e68-09b320b6001f","order_by":1,"name":"Nobuyuki Okahashi","email":"","orcid":"https://orcid.org/0000-0001-9582-355X","institution":"Osaka Universtiy","correspondingAuthor":false,"prefix":"","firstName":"Nobuyuki","middleName":"","lastName":"Okahashi","suffix":""},{"id":436874980,"identity":"e5e8cfe7-eba4-4b6b-9058-827ddda55f99","order_by":2,"name":"Shigetada Kawabata","email":"","orcid":"","institution":"Osaka University","correspondingAuthor":false,"prefix":"","firstName":"Shigetada","middleName":"","lastName":"Kawabata","suffix":""}],"badges":[],"createdAt":"2025-03-14 10:40:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6225383/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6225383/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81113753,"identity":"35d261c8-d5be-4b0d-b854-c31b64a83d79","added_by":"auto","created_at":"2025-04-22 11:11:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":288594,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTreg cell induction in mice with lower susceptibility to DSS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBALB/c mice from two vendors, SLC and Charles River (CHA), were treated with 4% DSS for 8 days. (A) Experimental approach. (B) DSS-induced weight loss and (C) Disease activity index (DAI) scores were recorded. (D) Representative flow cytometry plots depicting colonic FOXP3+, T-bet, and RORγt+ cells gated on CD4+ T-cells (CD45+CD11b-CD3+). Frequencies of (E) Treg (FOXP3+), (F) Th1 (T-bet+) (G) Th17 (RORγt+) cells are presented.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6225383/v1/54ed616060fcbe5dc5b11405.png"},{"id":81112899,"identity":"a4df5b57-c899-42ab-85ac-b5dec4ceb719","added_by":"auto","created_at":"2025-04-22 11:03:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":274296,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobiota-dependent Treg expansion in mice with lower susceptibility to DSS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proportions of colonic FOXP3+ cells in SLC mice cohoused with Charles River mice (CO-SLC) were compared with those of single-housed SLC mice (SLC). (A) Experimental approach. (B) DSS-induced DAI scores were recorded. (C) Representative flow cytometry plots depicting colonic FOXP3+ cells and (D) frequencies of Tregs (FOXP3+ cells). The values are presented as the means ± standard deviations (SDs). *P\u0026lt;0.05, **P\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6225383/v1/3f5b7b440d1079d897054f5c.png"},{"id":81112729,"identity":"aa878249-4881-4092-8c57-a61648c65aa9","added_by":"auto","created_at":"2025-04-22 10:55:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":316080,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe gut microbiota and these metabolites.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferences in microbiota taxa at the genus level among Charles River (CHA), SLC, and cohoused SLC (CO-SLC) mice were calculated prior to (A) and after (B) DSS treatment by linear discriminant analysis (LDA) effect size (LEfSe). (C) The calculation of Bray-Curtis dissimilarity quantify the dissimilarity of the pre- and post-DSS microbiota of each mice.\u003c/p\u003e\n\u003cp\u003e(D-F) Concentrations of cecal SCFAs in Charles River (CHA) and SLC mice on day 0 (pre-DSS) and day 8 (post-DSS). (D) Acetate, (E) butyrate, and (F) propionate were present. The values are presented as the means ± standard deviations (SDs). *P\u0026lt;0.05, **P\u0026lt;0.01, *** P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6225383/v1/cf97320be3cbdba5cab61c86.png"},{"id":81112727,"identity":"d9a581be-9e32-4420-acae-e3f1ce472a5d","added_by":"auto","created_at":"2025-04-22 10:55:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":343122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteins associated with colitis susceptibility.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDIA proteomics were conducted to compare host-derived colonic proteins between mice from two vendors with different susceptibilities. (A) Volcano plot showing upregulated and downregulated proteins (greater than twofold, p\u0026lt;0.05) in colon tissues from SLC mice and Charles River mice. The top 5 upregulated (red) and downregulated (green) genes are indicated. (B) Reactome pathway enriched in in SLC mice, which indicate disease amelioration. (C and D) Dot plots of GO terms enriched in SLC mice, which indicate disease amelioration (C), and enriched in Charles River mice, which indicate disease aggravation (D). The size of each dot represents the number of proteins from each GO term.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6225383/v1/3e4773bead550bfc40a2ef2e.png"},{"id":84220492,"identity":"d3e62637-c62e-4ffd-9ccf-2b14f42903d7","added_by":"auto","created_at":"2025-06-09 11:33:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1756531,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6225383/v1/fdb3e549-62b0-4256-8e21-cf4054c4de38.pdf"},{"id":81112730,"identity":"3ae185f2-690b-490b-90a1-5c8d96b1c220","added_by":"auto","created_at":"2025-04-22 10:55:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1506352,"visible":true,"origin":"","legend":"editorial policy checklist","description":"","filename":"editorial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6225383/v1/1a2994d05e898859b0a90681.pdf"},{"id":81112724,"identity":"6b2f73e6-4fee-4a83-94bb-4a2eed652712","added_by":"auto","created_at":"2025-04-22 10:55:39","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":750868,"visible":true,"origin":"","legend":"Supplementary Figure","description":"","filename":"supgutimm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6225383/v1/79a4f2dc856cf6a372961a65.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Eubiotic gut microbiota mitigate colitis via colonic Treg expansion.","fulltext":[{"header":"Main text","content":"\u003cp\u003eThe gastrointestinal tract is an organ that is in close proximity to and is continually exposed to the gut microbiota\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The gut microbiota can ferment host-indigestible dietary fibers and carbohydrate polymers, thereby producing beneficial metabolites that are indispensable for the survival of the mammalian host\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Consequently, humans have coevolved with the gut microbiota for millions of years. This symbiotic relationship demonstrates the deep biological integration between humans and the microbiota and is considered a \u0026ldquo;superorganism\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Among bacterial fermentation products, short-chain fatty acids (SCFAs) containing 1\u0026ndash;6 carbons play pivotal roles in shaping intestinal health. SCFAs fortify barrier function by promoting the production of antimicrobial agents and retaining epithelial barrier integrity; acetate, butyrate, and propionate are the most prominent of these\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In addition, increasing evidence suggests that SCFAs regulate mucosal and systemic immunity\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInflammatory bowel disease (IBD), chronic inflammatory diseases affecting the intestine, consists mainly of ulcerative colitis and Crohn\u0026rsquo;s disease; its incidence is increasing worldwide\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. IBD is a multifactorial disorder involving immune, genetic, and environmental factors, including dysbiosis of the gut microbiota. In addition to compositional microbial changes, functional changes that reduce the fecal concentrations of SCFAs have also been reported\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Atypical innate and adaptive immune responses to microbial antigens have widely been acknowledged. Notably, an imbalance of two novel subsets of lymphocytes, helper T (Th) cells and regulatory T (Treg) cells, contributes to colitis. Overactivation of Th17 and/or Th1 cells results in intestinal inflammation. In contrast, Foxp3\u0026thinsp;+\u0026thinsp;regulatory T (Treg) cells inhibit inappropriate induction of inflammatory T-cells, contributing to tuning immune tolerance\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Colonic Treg cells that reside in the lamina propria are distinct populations and are present at higher frequencies in the intestine than in other organs\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. A lower abundance of colonic Treg cells in germ-free mice has been observed, and the abundance was increased by the administration of \u003cem\u003eClostridia\u003c/em\u003e species\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, indicating that the gut microbiota and their metabolites promote the differentiation, accumulation, and function of Foxp3\u0026thinsp;+\u0026thinsp;Treg cells\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Butyrate induces intestinal regulatory Treg cell differentiation through histone deacetylase (HDAC) inhibition at the Foxp3 promoter. Propionate, but not acetate, has also been reported to increase histone H3 acetylation, which enhances Treg cell induction\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In addition to SCFA-induced Treg cell differentiation via HDAC inhibition, the combined administration of acetate, propionate and butyrate stimulates the differentiation of colonic Treg cells and confers protection against colitis through a G protein-coupled receptor (GPR) 43-dependent mechanism\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe have previously identified a wild-type mouse substrain that is resistant to dextran sulfate sodium (DSS)-induced colitis, and the disease severity was determined mainly by the gut microbiota, including SCFA producers\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Altered gut microbiota composition and function are commonly observed in IBD patients\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, but the significance of these changes remains unknown. To understand whether gut microbial dysbiosis is a main cause of disease onset in patients with colitis, we investigated the mechanism contributing to disease progression.\u003c/p\u003e\n\u003ch3\u003eSubstrain differences in colonic Treg accumulation\u003c/h3\u003e\n\u003cp\u003eBecause we demonstrated differences in the susceptibility of DSS-induced colitis among mice obtained from different vendors\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, we postulated that intestinal immune cells may be associated with disease severity. We used BALB/C mice (6\u0026ndash;7 weeks, female) purchased from two vendors: SLC and Charles River. Colitis was induced over 8 days by the administration of 4% (w/v) DSS in the drinking water (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The severity of DSS-induced colitis was investigated with respect to clinical parameters, weight loss, rectal bleeding, stool consistency and colon shortening. Consistent with our previous report, mice from SLC showed relatively fewer clinical symptoms, and mice from Charles River exhibited adequate colitis symptoms (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and C). To define the contributions of the immune cells that induce disease susceptibility, proportions of CD4\u0026thinsp;+\u0026thinsp;helper T-cells, known initiators and regulators of colitis in the colonic lamina propria, were compared in mice from the two vendors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD and supplementary Fig.\u0026nbsp;1). Compared with Charles River mice (high susceptibility), SLC mice (low susceptibility) exhibited expansion of colonic Tregs after DSS induction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). In contrast, the proportions of Th1 or Th17 cells were not significantly different between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF and G).\u003c/p\u003e \u003cp\u003eTo corroborate that Treg induction was due to differences in the gut microbiota between mouse substrains from different vendors, cohousing analysis was performed; this allows for the transfer of gut microbiota between mice. Low-susceptibility SLC mice were cohoused with high-susceptibility Charles River mice to allow horizontal bacterial transmission (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Cohoused SLC mice produced adequate colitis symptoms and that the severity of colitis in cohoused SLC mice was significantly greater than that in solo-housed SLC mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Cohoused SLC mice and solo-housed SLC mice are considered genetically identical. Consistently, compared with solo-housed SLC mice, cohoused SLC mice presented decreased Treg populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and D). Thus, cohousing-mediated microbiota transfer outweighed the effects of colitis persistence along with Treg inhibition.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGut microbiome and these metabolites against colitis\u003c/h2\u003e \u003cp\u003eTo determine the microbial changes induced by cohousing-mediated microbiota transfer, we explored the ability of the gut microbiota to reflect susceptibility to colitis. We previously reported that the gut bacterial composition of mice from SLC and Charles River were distinct even before DSS treatment, and Class Clostridia were significantly abundant in Charles River mice\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Reanalysis of our previously published microbiome dataset revealed that the composition of the gut microbiota in mice from disease-resistant solo-housed SLC mice and disease-susceptible Charles River and cohoused SLC mice analyzed using 16S rRNA sequences was comparable (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and B). Short-chain fatty acids producing \u003cem\u003eTuricibacter\u003c/em\u003e and \u003cem\u003eAlistipes\u003c/em\u003e, were significantly abundant in disease-resistant SLC mice after DSS challenge. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC shows the calculation of Bray-Curtis dissimilarity, a metric used to quantify the dissimilarity of the microbiota. Changes in microbiota between the pre- and post-DSS groups were not significantly different among the SLC, CO-SLC, and Charles River mice, suggesting that SLC mice changed their microbial composition because of DSS despite its disease resistance.\u003c/p\u003e \u003cp\u003eThe expansion of Tregs expressing Foxp3 suppresses inflammation in the colonic mucosa, thereby contributing to the attenuation of colitis. Short-chain fatty acids, microbial metabolites, are known as colonic Foxp3\u0026thinsp;+\u0026thinsp;Treg inducers\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. We obtained cecal contents from SLC and Charles River mice, and abundance of the main colonic SCFAs, acetate, propionate, and butyrate, was measured by GC/MS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F). The concentrations of acetate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) and butyrate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) in pre-DSS mice were not significantly different between Charles River and SLC mice, but the concentration of propionate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF) was significantly greater in SLC mice (p\u0026thinsp;=\u0026thinsp;6.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). Consistent with previous findings\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, DSS challenge caused a decrease in the levels of all three SCFAs, acetate, butyrate, and propionate, which were decreased in high-susceptibility Charles River mice (p\u0026thinsp;=\u0026thinsp;1.9\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, 2.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, and 5.8\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, respectively). Remarkably, low-susceptibility SLC mice maintained all their SCFA concentrations at a level equivalent to those of pre-DSS SLC and Charles River mice and significantly greater than those of colitic Charles River mice (p\u0026thinsp;=\u0026thinsp;3.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, 3.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, and 2.3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, respectively). The disease resistance of SLC mice may result from the abundance of one or a combination of the three SCFAs.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eColonic proteomics underlying the mechanisms of colitis resistance\u003c/h3\u003e\n\u003cp\u003eWe next conducted DIA proteomics analysis to compare the landscape of host-derived colonic proteins between mouse strains with different susceptibilities from two different vendors. Colon tissues were collected from Charles River mice and SLC mice and analyzed via DIA proteomics. Among the 6981 host-derived proteins identified, 49 molecules were more abundant (upregulated) in low-susceptibility SLC mice, whereas 61 molecules were more abundant (downregulated) in high-susceptibility Charles River mice (greater than twofold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Among these upregulated/downregulated proteins, the top five were selected based on their p value. The top proteins with the most highly upregulated (disease-ameliorate) expression were CBG, IRGM1, MSMO1, PAK1, RPL24, and ZHX2. Notably, PAK1 (P21-activated kinase 1) is a serine/threonine-protein kinase that regulates intestinal epithelial cell healing using SCFA \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWithin the fluctuating proteins, we analyzed the biological pathways and functions that affect disease resistance. Among the enriched disease-resistant reactome pathways, L13a-mediated translational silencing of ceruloplasmin expression was upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and Supplementary Fig.\u0026nbsp;3). Ceruloplasmin is an acute-phase protein that is responsible for copper transport and iron metabolism. Plasma iron homeostasis is maintained by strict regulation of gut iron absorption\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Iron ion binding was upregulated in the disease-ameliorating gene ontology (GO) terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) and copper ion binding was upregulated in the disease-aggravating GO terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). This is consistent with previous studies in which reduced iron absorption and increased serum copper concentration along with CRP have been shown in with the IBD patients\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Iron has proven to be indispensable for gut SCFA production\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Collectively, our results demonstrate that silencing ceruloplasmin expression restricts iron metabolism, thereby increasing SCFA production as well as preventing colitis progression.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe underlying cause of IBD is challenging to determine, as it involves a combination of genetic predispositions, immune dysregulation, and environmental factors. Most epidemiological studies have focused on patients with active or treated disease, indicating that inflammation and tissue damage have already developed. This makes it difficult to determine whether the observed changes are attributable to the initiation and subsequent progression of disease. It is crucial to identify the factors that influence disease onset. We showed that in mice with eubiotic gut microbiota, which are thus less susceptible to DSS-induced colitis, the expansion of the colonic Treg population by the gut microbiota and microbial SCFA metabolites protect against colitis. In contrast, mice that have noneubiotic gut microbiota but are genetically identical to mice with lower susceptibility show lower Treg levels, thus leading to the development of colitis. In IBD patients, fecal microbiota transplantation (FMT) has shown potential as a therapeutic intervention\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. FMT is a technique that aims to improve the diversity and abundance of the gut microbiota by transplanting healthy feces, which leads to the restoration of gut microbial eubiosis. Notably, the eubiotic microbiota plays a pivotal role in the onset and progression of colitis.\u003c/p\u003e \u003cp\u003eSCFAs are prime examples of beneficial metabolites produced from microbiota-accessible carbohydrates. Among all SCFAs, butyrate is reported to be utilized the most efficiently\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. We showed that the concentrations of acetate, butyrate, and propionate in the cecal contents are correlated with disease development. Bacterial cross-feeding influences the final balance of SCFA production and the effective utilization of substrates that reach the gut\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The butyrogenic effects of dietary fiber are the result of cross-feeding between butyrate-producing and nonbutyrate-producing strains. For example, butyrogenic bacteria produce butyrate by utilizing fermentation byproducts, such as acetate, generated during the initial breakdown of dietary carbohydrates. The production levels of acetic acid and propionic acid are also considered important. SCFAs promote intestinal epithelial cell wound healing through PAK1 and MFGE8 signaling\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. PAK1 belongs to a serine‒threonine kinase family and contributes to the regulation of crypt homeostasis during intestinal inflammation by controlling Notch1\u003csup\u003e33\u003c/sup\u003e. PAK1 was one of the top 5 proteins related to disease resistance, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Together, these findings suggest that SCFAs produced by gut bacteria suppress inflammation via the PAK1 pathway, enabling SLC mice to exhibit resistance to colitis. Our study identified that mice with eubiotic gut microbiota show lower susceptibility to DSS-induced colitis and expansion of the colonic Treg population via the gut microbiota and microbial SCFA metabolites. Silencing ceruloplasmin expression restricts iron metabolism, thereby increasing SCFA production. Resistance to colitis was induced in a genetics-independent manner. Therefore, on the basis of the effects of the gut microbiota and these metabolites on host immune responses, our results may reveal the importance of eubiotic gut microbiota in the prevention of colitis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMice\u003c/h2\u003e \u003cp\u003eBALB/c mice were obtained from SLC Japan (BALB/cCrSLC, Shizuoka, Japan) and Charles River Laboratories Japan (BALB/cAnNCrlCrlj, Kanagawa, Japan) and housed under SPF conditions. For the cohousing experiment, four-week-old female BALB/c mice purchased from SLC and Charles River were cohoused for 4 weeks as described previously\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Acute colitis was induced by the oral administration of 4% (w/v) DSS (MP Biomedicals, Santa Ana, CA, USA) in the drinking water for eight days per the experiment. The progression of colitis was assessed as previously described\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. All animal experiments were approved by the Animal Care and Use Committee of Osaka University Graduate School of Dentistry (R05-009-0). We confirm that all the animal experiments were performed in accordance with the ARRIVE guidelines 2.0.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIsolation of lymphocytes\u003c/h3\u003e\n\u003cp\u003eLamina propria cells were isolated from the colons. The dissected colon tissues were treated with 1 mM EDTA at room temperature for 1 h to remove epithelial cells and then minced into Hanks\u0026rsquo; balanced salt solution with collagenase type I (200 U/ml, Wako, Osaka, Japan) and DNase (20 U/ml, Worthington Biochemical, Lakewood, NJ, USA) at 37\u0026deg;C for 30 min. After filtering, lymphocytes were collected.\u003c/p\u003e\n\u003ch3\u003eFlow cytometry\u003c/h3\u003e\n\u003cp\u003eTo analyze the percentage of Th cells in the colon, the lymphocytes isolated as described above were stained according to the manufacturer\u0026rsquo;s guidelines. For intercellular staining of cells, a mouse Treg cell staining kit (eBioscience, San Diego, CA, USA) was used. The following antibodies were obtained from Thermo Fisher Scientific (MA, USA): anti-CD45 (30-F11), anti-CD11b (M1/70), anti-CD3 (145-2C1), anti-CD4 (RM4-5), anti-Foxp3 (FJK-16S), anti-T-bet (4B10), and anti-Rorγt (B2D), and Fixable Viability Dye (Supplementary Fig.\u0026nbsp;1). Flow cytometer we used was BD FACS Canto II (NJ, USA).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eShort-chain fatty acid quantification\u003c/h2\u003e \u003cp\u003eShort-chain fatty acids in the cecal contents were derivatized by pentafluorobenzylation and quantified via gas chromatography‒mass spectrometry (GC‒MS) as previously described with minor modifications\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In brief, metabolites were extracted from the cecal contents and mixed with the deuterium-labeled internal standards acetate-d3 (Cambridge Isotope Laboratories, Tewksbury, MA, USA), butyrate-d7 (CDN isotopes, Quebec, Canada), and propionate-d5 (Cambridge Isotope Laboratories). The derivatized SCFAs were analyzed via a GC‒MS QP2020 (Shimadzu Corporation, Kyoto, Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProteomics\u003c/h2\u003e \u003cp\u003eIntestinal proteins were extracted from the colon tissues soaked in lysate (2% sodium deoxycholate, 50 mM Tris, and 150 mM NaCl) containing a protease inhibitor cocktail (cOmplete, Sigma Aldrich, St. Louis, MO, USA), homogenized in a MagNA Lyser (Roche, Basal, Switzerland), and sonicated on ice for 60 min. The supernatants were quantified by a BCA Bradford assay (Bio-Rad, San Diego, CA, USA). The purified samples were analyzed by using UPLC (Vanquish Neo; Thermo Fisher Scientific) and MS (Orbitrap Exploris 480; Thermo Fisher Scientific). Peptides were identified via the data-independent acquisition (DIA) technique. The raw data were analyzed and visualized using Scaffold DIA (V 3.4.1).\u003c/p\u003e \u003cp\u003eUpregulated and downregulated protein expression (greater than twofold, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were selected as inputs for functional analysis. Reactome pathway analysis was performed using g:Profiler (ve112_eg59_p19_25aa4782) and reactome pathway database (v91). GO enrichment analysis was performed using UniProt (v2024_06), and org.Mm.eg.db (v3.20.0).\u003c/p\u003e \u003cp\u003eConverted genes were mapped in the and GO Resource (v40635).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReanalysis of fecal metagenome samples\u003c/h2\u003e \u003cp\u003eWe reanalyzed the publicly available fecal metagenomes from our previous study, which were deposited in the DDBJ under accession number PRJDB15999.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eComparisons of two groups were performed using an unpaired \u003cem\u003et\u003c/em\u003e (parametric) test or a Mann‒Whitney \u003cem\u003eU\u003c/em\u003e (nonparametric) test. Differences among more than three groups were evaluated using one-way analysis of variance for parametric analysis or the Kruskal‒Wallis test for nonparametric analysis followed by Bonferroni correction (parametric) or Steel‒Dwass correction (nonparametric). The normality of the data was analyzed via the Kolmogorov‒Smirnov test. The homogeneity of variance was analyzed via the F test (two groups) or Bartlett test (more than three groups). The error bars represent the standard de viation of a dataset. All the statistical analyses were performed with EZR statistical software.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing financial interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAdditional information\u003c/h2\u003e \u003cp\u003eSupplementary Information is available for this paper.\u003c/p\u003e \u003cp\u003eCorrespondence and requests for materials should be addressed to Eri Ikeda.\u003c/p\u003e \u003cp\u003ePeer review information Nature thanks the anonymous reviewers for their contribution to the peer review of this work.\u003c/p\u003e \u003cp\u003eReprints and permissions information is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.nature.com/reprints\u003c/span\u003e\u003cspan address=\"http://www.nature.com/reprints\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eEI and SK designed experiments. EI performed animal experiments, immune analysis, proteomics, and metagenomics. NO analyzed the metabolites production of the colon. EI was a major contributor in writing the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe flow cytometry analysis was supported by the Center for Medical Research and Education, Graduate School of Medicine, Osaka University. This work was also supported by the Osaka University program for the support of networking among present and future researchers. This work was financially supported by the Japan Society for the Promotion of Science, and the Futoku Foundation.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe proteome data were deposited in the Japan Proteome Standard Repository under project no. 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Gut microbiota, metabolites and host immunity. \u003cem\u003eNat Rev Immunol\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 341-352, doi:10.1038/nri.2016.42 (2016).\u003c/li\u003e\n\u003cli\u003eFrick, A.\u003cem\u003e et al.\u003c/em\u003e A Novel PAK1-Notch1 Axis Regulates Crypt Homeostasis in Intestinal Inflammation. \u003cem\u003eCell Mol Gastroenterol Hepatol\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 892-907 e891, doi:10.1016/j.jcmgh.2020.11.001 (2021).\u003c/li\u003e\n\u003cli\u003eYasuda, S.\u003cem\u003e et al.\u003c/em\u003e Elucidation of Gut Microbiota-Associated Lipids Using LC-MS/MS and 16S rRNA Sequence Analyses. \u003cem\u003eiScience\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 101841, doi:10.1016/j.isci.2020.101841 (2020).\u003c/li\u003e\n\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":"ceruloplasmin, inflammatory bowel disease, regulatory T-cells, short-chain fatty acids, ulcerative colitis ","lastPublishedDoi":"10.21203/rs.3.rs-6225383/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6225383/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAberrant gut microbiota has been linked to a disrupted immune response, leading to intestinal pathological conditions\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, the causality and mechanisms have remained unclear.\u003c/p\u003e \u003cp\u003eHere we used a wild-type mouse substrain resistant to dextran sulfate sodium (DSS)-induced colitis to understand the characteristics of the eubiotic microbiota and the mechanisms underlying colitis resistance. Colitis-resistant mice presented a significantly greater colonic Foxp3\u0026thinsp;+\u0026thinsp;regulatory T (Treg) population after DSS challenge, while numbers of helper T1 and 17 cells were comparable. Cohousing experiments revealed that Treg expansion was induced by the gut microbiota of colitis-resistant mice, and mice with eubiotic microbiota maintained high concentrations of three microbial metabolites, SCFAs (acetate, butyrate, and propionate). Furthermore, proteomics revealed that iron binding and iron-metabolizing ceruloplasmin silencing were upregulated relating disease-resistance. Of note, iron plays a pivotal role to SCFA-producing bacteria. Taken together, our results reveal that silencing ceruloplasmin expression restricts iron metabolism, thereby increasing SCFA production. The eubiotic gut microbiota and combinations of SCFAs contribute to Treg induction, which helps control colitis progression.\u003c/p\u003e","manuscriptTitle":"Eubiotic gut microbiota mitigate colitis via colonic Treg expansion.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 10:55:34","doi":"10.21203/rs.3.rs-6225383/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":"aa7349ed-50c9-421d-8c64-3f8233701a3b","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46518792,"name":"Health sciences/Gastroenterology/Gastrointestinal diseases/Inflammatory bowel disease/Ulcerative colitis"},{"id":46518793,"name":"Health sciences/Gastroenterology/Gastrointestinal models"},{"id":46518794,"name":"Health sciences/Gastroenterology/Gastrointestinal diseases/Dysbiosis"},{"id":46518795,"name":"Health sciences/Pathogenesis/Immunopathogenesis/Adaptive immunity/Immune tolerance/Peripheral tolerance"}],"tags":[],"updatedAt":"2025-06-09T11:25:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-22 10:55:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6225383","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6225383","identity":"rs-6225383","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}