Characterization of fungal and bacterial Dysbiosis in Crohn's Disease Patients with Intestinal Fibrosis

Characterization of fungal and bacterial Dysbiosis in Crohn's Disease Patients with Intestinal Fibrosis | 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 Research Article Characterization of fungal and bacterial Dysbiosis in Crohn's Disease Patients with Intestinal Fibrosis Junjian Sun, Junjie Lin, Shu Wang, Jiayun Wang, Lu Wang, Yanqiu Yu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6966692/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 Background Intestinal fibrosis is a serious complication of Crohn's disease (CD) that often leads to strictures and surgery. Although the bacterial microbiome's role in CD pathogenesis has been extensively characterized, the fungal microbiota's contribution to fibrotic progression remains poorly defined. Growing evidence suggests fungi may influence fibrosis through immune and metabolic pathways. This study systematically evaluated compositional and functional alterations in the gut mycobiota associated with CD-related intestinal fibrosis. Method Fecal samples from well-characterized CD patients with (n = 22) and without (n = 19) intestinal fibrosis underwent ITS and 16S rRNA gene sequencing (Illumina MiSeq platform, V4 region). Bioinformatics analysis included: (1) α-diversity assessment; (2) β-diversity evaluation via unweighted UniFrac distances with PERMANOVA; (3) differential abundance analysis using LEfSe (LDA score > 2.0, P < 0.05); and (4) Spearman's rank correlation for fungal taxa-clinical parameter associations. Functional profiling was performed through phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt2) with COG, KEGG, and MetaCyc databases. Results In this study, fecal samples from well-characterized CD patients with (n = 22) and without (n = 19) intestinal fibrosis underwent ITS and 16S rRNA gene sequencing. CD patients with intestinal fibrosis demonstrated significant alterations in gut fungal ecology, characterized by reduced α-diversity (Chao1 index, P < 0.05) and distinct β-diversity clustering (PERMANOVA, R²=0.05, P = 0.01). The stricturing group showed marked enrichment of Alternaria ( P = 0.03) and an increased Basidiomycota/Ascomycota ratio, suggesting phylum-level shifts in fungal composition. Notably, Alternaria and Penicillium abundances exhibited significant negative correlations with systemic inflammatory markers (WBC counts, P < 0.05). This study also found various interactions between intestinal fungi and bacteria. Functional analyses revealed concurrent upregulation of pro-fibrotic pathways including LOXL-mediated extracellular matrix remodeling and lipid metabolism, alongside impaired protective functions evidenced by suppressed taurocholate degradation (all P < 0.05). Conclusion This study reveals gut fungal dysbiosis with specific taxonomic and functional shifts in CD-associated fibrosis, highlighting Alternaria enrichment and LOXL-mediated ECM remodeling as potential therapeutic targets. These findings provide new insights into microbial contributions to intestinal fibrogenesis. (Chinese Clinical Trial Registry Center, ChiCTR2100054258, Registered 12 December 2021) Intestinal Fungi Crohn’s disease Intestinal fibrosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Crohn’s disease (CD) is a chronic relapsing inflammatory disorder of the gastrointestinal tract. As the disease progresses, the incidence of intestinal strictures gradually increases, with up to 70% of CD patients developing at least one stricture within 10 years of diagnosis, which can lead to intestinal obstruction or even perforation 1 , 2 . The pathological basis of intestinal strictures is intestinal fibrosis. Despite the recent clinical application of various biologics, there is currently no evidence that these agents can reverse established fibrosis 3 . Intestinal strictures and their complications, such as perforation, remain major challenges in CD management 4 . While the relationship between bacterial communities and CD has been extensively studied, the role of fungi-a critical component of the gut microbiome-in CD, particularly in patients with intestinal fibrosis, remains poorly understood. In recent years, the role of gut dysbiosis in CD pathogenesis has garnered significant attention 5 . However, existing research has primarily focused on bacterial communities, with limited investigation into fungal populations. Although fungi represent only 0.1% of the total gut microbiota, they play crucial roles in maintaining intestinal homeostasis through multiple mechanisms 6 . Structurally, the healthy human gut is primarily colonized by five major fungal phyla, with Ascomycota and Basidiomycota being predominant, along with dominant genera such as Candida and Saccharomyces , which exhibit a gradient distribution from the ileum to the colon 7 . Functionally, fungi contribute to host immunity by stimulating IgA secretion and regulating cytokine networks, participate in metabolic processes such as complex polysaccharide degradation and vitamin synthesis, and engage in cross-kingdom interactions with bacteria to maintain ecological balance in the gut 8 – 10 . Notably, growing evidence suggests that fungal dysbiosis is closely linked to CD pathogenesis 11 . Our previous study revealed distinct fungal microbiota profiles between the healthy controls and CD groups, with significant differences emerging at the genus level rather than the phylum level. Notably, the Candida genus was significantly enriched in patients with active CD compared to healthy controls 12 . Some studies have revealed significantly reduced fungal diversity in CD patients, along with overgrowth of opportunistic pathogens like Candida albicans and elevated anti-Saccharomyces cerevisiae antibodies (ASCA) 13 , 14 . Intriguingly, recent work implicates specific fungi (e.g., Malassezia ) in CD heterogeneity through CARD9-mediated aberrant immune responses, offering new insights into disease mechanisms 15 , 16 . However, the role of fungi in CD-associated intestinal fibrosis, particularly their contribution to stricture formation, remains largely unexplored. To address this gap, our study focuses on characterizing the gut mycobiota in CD patients with fibrostenotic strictures. By analyzing fecal samples from stricturing and non-stricturing CD patients using high-throughput sequencing and bioinformatics, we aim to identify fungal signatures unique to fibrostenotic CD, elucidate potential links between fungal functional shifts and fibrosis, and explore fungal biomarkers for disease prediction. This work will not only advance understanding of fungal contributions to CD-associated intestinal fibrosis but may also provide novel theoretical foundations for early diagnosis and targeted therapies against intestinal fibrosis. Methods Patients and surgical samples collection This single-center prospective cross-sectional study enrolled 41 CD patients (19 with intestinal fibrotic strictures and 22 without) from the Department of Gastroenterology at Jiangsu Province Hospital between August 2024 and February 2025, collecting fecal samples from all participants for analysis. CD diagnoses were based on the European Crohn's and Colitis Organisation (ECCO) guidelines 17 , while intestinal strictures were identified using the 2024 Stricture Treatment and Anti-fibrosis Research (STAR) Consortium criteria 4 . Exclusion criteria included: age < 18 years, recent colonoscopy within 4 weeks prior to enrollment, and antibiotic use within 3 months before enrollment. Table 1 summarizes patient characteristics, including age, sex, Body Mass Index (BMI), Montreal classification, platelet count (PLT), hematocrit (HCT), Albumin (ALB), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), fecal calprotectin (FC), and current medications. This study was conducted in full compliance with the ethical principles outlined in the Declaration of Helsinki. The study was approved by the Ethics Committee of the First Affiliated Hospital with Nanjing Medical University (Approval No. 2023-SR-149), and all participants provided written informed consent prior to enrollment. This study was registered at the Chinese Clinical Trial Registry (Registration number: ChiCTR2100054258). Table 1 Basic characteristics of CD patients. Characteristic Non-stricturing CD (n = 19) Stricturing CD (n = 22) P Age,y, median(IQR) 27 (21–30) 30 (26–37) 0.10 Female, n(%) 5 7 0.97 BMI,kg/m 2 , median(IQR) 20.20 (19.11–23.87) 20.41 (18.80-24.44) 0.95 Smoking, n(%) 0 4 0.11 Drinking, n(%) 1 1 1 Montreal classification, n A1/ A2/A3 0 /17/2 0 /15/7 0.17 L1/L2/L3/L4 10/2/7/0 13/2/6/1 0.88 WBC, 10^9/L, mean (SD) 6.42 ± 1.78 5.75 ± 2.02 0.26 PLT,10^9/L, median (IQR) 245.6 (194.50-275.75) 253.24 (217.50-286.50) 0.74 HCT, %, mean (SD) 41.09 ± 4.59 39.396 ± 5.75 0.22 ALB, g/L, mean (SD) 40.70 ± 5.82 37.95 ± 5.21 0.19 CRP,mg/L, median (IQR) 6.2 (1.9–13.1) 8.5 (2.8–17.4) 0.86 ESR, mm/h, median (IQR) 21.63 (5–22) 24.36 (5-34.25) 0.77 FC, µg/g, median (IQR) 433.89 (55.45–875.50) 561.24 (210.20-862.85) 0.43 Current medication 0.25 5-aminosalicylic acid 4 8 Azathioprine 0 2 Biologics 15 11 None 0 1 Comparing Non-stricturing CD (n = 19) with Stricturing CD (n = 22) using Mann–Whitney U-test for skewed distributed continuous variables, independent t-test for normal distributed continuous variables, and Chi-square/Fisher exact test for categorical variables. IQR = Interquartile Range, SD = Standard Deviation. Stool Collection and DNA Extraction Fecal samples were homogenized and stored at -80°C for subsequent analysis. DNA extraction was performed using the TianGen Magnetic Bead DNA Extraction Kit (TianGen, China, Catalog #: DP712). Specifically, 250 mg of fecal sample was mixed with 500 µL of buffer SA, 100 µL of buffer SC, and 0.25 g of grinding beads. The mixture was homogenized using a TGrinder H24 tissue homogenizer (OSE-TH-01) at 6 m/s for 30 s, with a 30 s interval, for two cycles, followed by heat lysis at 70°C for 15 min. After centrifugation at 12,000 rpm for 1 min, the supernatant was collected and mixed with 200 µL of buffer SH by vortexing for 5 s, then incubated at 4°C for 10 min. The mixture was centrifuged again at 12,000 rpm for 3 min, and the supernatant was combined with 500 µL of buffer GFA and inverted to mix thoroughly. Magnetic bead suspension G was added, and the mixture was oscillated for 5 min before proceeding with subsequent steps according to the manufacturer's instructions until extraction was complete. DNA concentration was measured using a NanoDrop spectrophotometer (Thermo Fisher Scientific). Additionally, FC levels were determined using the Calpro AS ELISA kit. Samples were diluted at a 1:50 ratio with Calpro EasyExtract and processed according to the manufacturer's protocol. Internal Transcribed Spacer sequencing (ITS sequencing) ITS2 region: Forward primer ITS1FI2 (5'-GTGARTCATCGAATCTTTG-3'), reverse primer ITS2 (5'-TCCTCCGCTTATTGATATGC-3'). ITS1 region: Forward primer F (5'-GAACCWGCGGARGGATCA-3'), reverse primer R (5'-GCTGCGTTCTTCATCGATGC-3'). Sample-specific barcodes and universal sequencing adapters were added to the 5' end of each primer. The PCR reaction was performed in a total volume of 25 µL, containing 25 ng of template DNA, 12.5 µL of PCR Premix, 2.5 µL each of forward and reverse primers, and PCR-grade water to adjust the final volume. The ITS amplification protocol was as follows: Initial denaturation at 98°C for 30 sec; 32 cycles of amplification (98°C for 10 sec, 54°C for 30 sec, 72°C for 45 sec); Final extension at 72°C for 10 min. PCR products were verified by 2% agarose gel electrophoresis. Throughout the DNA extraction process, ultrapure water was used as a negative control in place of sample solutions to exclude false-positive results. PCR products were purified using AMPure XT beads (Beckman Coulter Genomics, USA) and quantified with Qubit (Invitrogen, USA). After amplicon library construction, fragment size and concentration were assessed using the Agilent 2100 Bioanalyzer (Agilent, USA) and the Illumina Library Quantification Kit (Kapa Biosciences, USA), respectively. Finally, paired-end sequencing (PE250) was performed on the NovaSeq platform. Statistical analysis After paired-end sequencing on the Illumina NovaSeq platform, the raw data were initially demultiplexed based on sample-specific barcodes followed by removal of both barcode and primer sequences. The paired-end reads were then merged using PEAR software, and high-quality clean tags were obtained through stringent quality control with fqtrim (v0.94), with subsequent chimera filtering performed using Vsearch (v2.3.4). DADA2 was utilized to generate amplicon sequence variants (ASVs) for the ITS and 16S sequences for each sample, which were normalized by subsampling to the minimum sequencing depth per sample in QIIME2 for subsequent diversity analyses. Both α-diversity and β-diversity indices were calculated and visualized using R (v4.4.2). For taxonomic classification, the feature-classifier plugin in QIIME2 was employed to annotate sequences against the RDP and UNITE reference databases, with fungal community composition analyzed based on relative abundance (calculated as counts of specific fungi divided by total counts). Results Alterations in gut fungal community diversity characteristics in CD patients with intestinal fibrosis The findings demonstrate significant alterations in gut fungal community diversity among CD patients with intestinal fibrosis. The Venn diagram showed that 616 ASVs were generated from non-stricturing samples and 307 ASVs were generated from the structuring samples, with 125 ASVs shared between both groups (Fig. 1 A). β-diversity analysis based on PERMANOVA testing of Unweighted UniFrac distances demonstrated significant intergroup differences (R²=0.05, P = 0.01), and Principal Coordinate Analysis (PCoA) confirmed distinct clustering patterns (Fig. 1 B). Assessment using three α-diversity indices (observed species, Shannon diversity index, and Chao1 richness index) revealed decreased trends across all metrics in the stricture group, with the Chao1 index showing statistically significant reduction ( P < 0.05), indicating diminished species richness associated with stricture formation (Fig. 1 C-E). These findings suggest that stricture patients exhibit a characteristic “diversity depletion” pattern in their fungal communities, reflecting a transition from complex and stable to simplified and vulnerable ecosystem structures. From a pathophysiological perspective, the loss of these fungal populations could hypothetically impair immune regulation via the “microbiota-immune-fibrosis” axis, potentially contributing to disease progression 18 . Alterations in fungal microbiota composition Our study revealed that Ascomycota and Basidiomycota constituted the predominant fungal phyla in the gut microbiota, with Ascomycota being the most abundant in both the stricture (70.45%) and non-stricture groups (67.78%) (Fig. 2 A-B). Notably, the stricture group exhibited a significantly higher Basidiomycota/Ascomycota ratio (0.32 vs. 0.27). LEfSe analysis identified significant enrichment of Alternaria ( P = 0.03) in the stricture group, alongside marked reductions in Eurotium and Guehomyces (both P = 0.02) (Fig. 2 C). Although not statistically significant, trends of increased relative abundance were observed for Malassezia, Meyerozyma, Hypocrea, Candida , and Penicillium in stricture patients, whereas Saccharomyces, Cryptococcus , and Eupenicillium showed decreased abundance (Fig. 2 D). The observed alterations in specific fungal taxa could potentially relate to intestinal fibrotic progression, but this preliminary association warrants further mechanistic validation. The enrichment of Alternaria in stricture patients warrants further investigation as a possible microbial signature associated with stricture status, though its utility as a clinical biomarker would require validation in independent cohorts. Correlation analysis between gut fungal taxa and clinical parameters Spearman’s rank correlation analysis revealed significant associations between specific gut fungal taxa and clinical indicators (Fig. 3 A). Notably, the relative abundances of Alternaria ( R = -0.32, P = 0.04) and Penicillium ( R = -0.38, P = 0.01) demonstrated significant negative correlations with WBC, while Eurotium showed a significant positive correlation ( R = 0.39, P = 0.01). Furthermore, the abundances of Malassezia ( R = 0.39, P = 0.01) and Cryptococcus ( R = 0.42, P = 0.01) were positively associated with PLT. These findings reveal significant correlations between specific gut fungal taxa and systemic inflammatory markers, implying a potential link between gut fungal community variations and inflammatory responses in CD patients with intestinal fibrosis. Correlation between Intestinal Fungal and Bacterial Composition We further observed changes in bacterial composition in CD patients with (n = 22) and without (n = 19) intestinal fibrosis (Fig. 4 A). We systematically evaluated the interkingdom relationships between fungal and bacterial taxa at both phylum and genus levels in stricturing versus non-stricturing phenotypes. At the phylum level, Ascomycota showed a negative correlation with Basidiomycota and Zygomycota (Fig. 3 B). Actinobacteriota was positively correlated with Ascomycota and negatively correlated with Basidiomycota (Fig. 3 B). At the genus level, we observed a relationship between the 27 main bacteria (found in more than 50% of the samples and had a relative abundance > 0.1%) and 8 main fungal genera. The complex relationships between bacteria and fungi are illustrated in Fig. 4 C. Functional prediction analysis To investigate functional differences in fungal communities between stricturing and non-stricturing CD patients, we conducted systematic functional prediction 19 and comparative analyses based on the COG database (Fig. 5 A). The results revealed distinct metabolic signatures in the fibrostenotic group, with significant enrichment of genes encoding key metabolic enzymes, including acetyl-CoA carboxylase and NADPH-dependent ferric reductase ( P < 0.05), suggesting that fungal metabolic differences, particularly in lipid metabolism and oxidative stress responses, may be relevant to intestinal fibrosis. Conversely, the non-stricturing group exhibited higher abundance of chitinase genes ( P < 0.05), potentially reflecting distinct ecological functions. While these predictions highlight metabolic disparities, their direct relevance to fibrosis requires experimental validation. KEGG-based functional prediction further identified fibrosis-related pathways significantly upregulated in stricturing patients, including lysyl oxidase-like proteins 2/3/4 (LOXL2/3/4)-key enzymes mediating ECM crosslinking-methylmalonyl-CoA transferase (involved in short-chain fatty acid metabolism), and ABC-type iron transport systems ( P < 0.05) (Fig. 5 B). These predicted functional differences suggest possible microbial contributions to fibrosis through ECM remodeling, metabolic and iron homeostasis pathways. Intriguingly, elevated chitinase in non-stricturing patients may indicate distinct host-microbe interactions. Notably, previous studies implicate circulating LOXL2 in ECM deposition 20 , warranting investigation of its biomarker potential for fibrosis monitoring. However, the therapeutic implications of these predicted pathways require experimental validation. MetaCyc pathway analysis further highlighted metabolic reprogramming in stricturing patients: while non-stricturing samples showed enrichment in tryptophan/branched-chain amino acid biosynthesis (e.g., L-isoleucine synthesis) and fatty acid β-oxidation ( P < 0.05), indicating robust nutrient metabolism, stricturing samples exhibited upregulated cell wall biosynthesis (e.g., UDP-N-acetylmuramate pathway) and lipid metabolism (e.g., palmitoleic acid synthesis), alongside downregulated taurocholate degradation-a pathway linked to mucosal protection (Fig. 5 C). This metabolic shift is consistent with a potential pro-inflammatory state and impaired barrier integrity, though direct evidence linking these pathways to inflammation or barrier dysfunction requires further validation. These findings highlight distinct microbial metabolic patterns associated with intestinal strictures, expanding our understanding of potential microbiome-metabolite interactions in fibrosis. Discussion Our study provides a comprehensive analysis of the intestinal fungal community and its predicted metabolic functions in CD patients with intestinal fibrosis. We observed that stenotic CD patients exhibit significant fungal dysbiosis, characterized by altered community structure and enriched metabolic pathways (e.g., ECM remodeling, lipid metabolism). While these findings suggest possible links between fungal ecology and fibrotic progression, further mechanistic studies are needed to establish causal relationships. This work lays the foundation for future investigations into fungal-host interactions in fibrosis. Our study found that patients in the stenosis group exhibited a significant reduction in fungal α-diversity, particularly with the Chao1 index indicating a decrease in species richness, which aligns with previous reports of diminished microbial diversity in inflammatory bowel disease 21 , 22 . Notably, β-diversity analysis revealed distinct fungal community structures between stenotic and non-stenotic patients, likely reflecting microbial selection pressures specific to fibrotic progression. Reduced community diversity, often considered a marker of ecosystem instability, may lead to a “diversity collapse” in the gut microenvironment, resulting in the loss of keystone fungal species and compromised microbial resilience 18 . From a pathophysiological standpoint, the observed reduction in fungal diversity may contribute to fibrotic progression through several interconnected mechanisms. The diminished presence of beneficial fungi such as Saccharomyces boulardii 23 compromises their crucial anti-inflammatory and mucosal protective functions, while the disrupted equilibrium in fungal-bacterial crosstalk induces microenvironmental alterations 6 . Furthermore, the dysregulation of critical fungal-mediated metabolic networks, especially those governing immunomodulatory pathways, may create a permissive milieu for fibrosis development. At the community composition level, our study revealed an elevated Basidiomycota/Ascomycota ratio in stenosis patients, partially consistent with previous findings regarding fungal community alterations in IBD 24 , 25 . Particularly noteworthy was the significant enrichment of Alternaria in the stenosis group, a finding with important clinical implications. As a common environmental saprophytic fungus, certain Alternaria species are known to produce allergenic proteins and mycotoxins, with established associations to respiratory allergic diseases 26 . Our study reports novel observations of Alternaria enrichment in CD-associated intestinal stenosis in our cohort. Notably, we observed an inverse correlation between Alternaria abundance and systemic inflammatory markers (WBC count). While the exact mechanisms remain speculative, previous studies suggest several plausible explanations that warrant further investigation. Alternaria mycotoxins have demonstrated epithelial-damaging effects in vitro 27 ; certain fungal components are known to elicit Th2-skewed immune responses 28 ; and microbial metabolites can influence fibroblast activation in other systems 29 . However, the functional consequences of Alternaria enrichment in intestinal fibrosis require direct experimental validation. The observed correlations between intestinal fungal and bacterial taxa in CD patients with or without fibrosis suggest potential microbial interactions influencing disease progression. The negative correlation between Ascomycota and Basidiomycota/Zygomycota at the phylum level may indicate competitive ecological niches, while the positive association between Actinobacteriota and Ascomycota could imply synergistic metabolic or immunomodulatory effects. The complex bacterial-fungal network at the genus level highlights the need for further mechanistic studies to elucidate whether these microbial shifts contribute to fibrotic complications or merely reflect disease severity 24 . Understanding these interactions may open new therapeutic avenues targeting the gut mycobiome-bacteriome axis in CD. Functional prediction analysis yielded deeper insights into the pathological roles of fungal communities 30 . The enrichment of metabolic enzymes such as acetyl-CoA carboxylase and NADPH-dependent ferric reductase in the stenosis group suggests that lipid metabolism and oxidative stress may play pivotal roles in fibrogenesis. As the rate-limiting enzyme in fatty acid synthesis, upregulated acetyl-CoA carboxylase could lead to lipid accumulation, while lipid peroxidation products like 4-hydroxynonenal (4-HNE) have been shown to activate myofibroblasts 31 . NADPH-dependent ferric reductase participates in iron metabolism, and iron overload may promote reactive oxygen species generation, exacerbating tissue damage and fibrosis 32 . KEGG analysis revealed particularly clinically significant enrichment of LOXL2/3/4 pathways. LOX family enzymes serve as crucial mediators of ECM crosslinking, directly involved in collagen fiber maturation and stabilization 33 – 35 . Our findings suggest that gut fungi may promote ECM deposition by upregulating LOX expression, providing novel perspectives for anti-fibrotic strategy development. Notably, recent studies have demonstrated the efficacy of LOXL2 inhibitors (e.g., PXS-5153A) in hepatic fibrosis models 36 , 37 , and our results provide a theoretical foundation for extending such therapeutics to intestinal fibrosis. MetaCyc pathway analysis revealed distinct metabolic reprogramming features in stenosis patients. The upregulation of cell wall component biosynthesis pathways likely reflects fungal adaptation to the pro-inflammatory environment through enhanced protective mechanisms, while downregulation of taurine degradation pathways may compromise its anti-inflammatory and antioxidant effects. As a compound with demonstrated membrane-stabilizing, calcium homeostasis-regulating, and free radical-scavenging properties, reduced taurine metabolism could exacerbate oxidative damage and inflammatory responses 38 . These findings collectively delineate a “pro-fibrotic” metabolic landscape, offering novel insights into how microbial metabolism influences host pathological processes. While this study provides preliminary insights into the fungal microbiota characteristics in CD-related intestinal fibrosis, several limitations should be addressed. First, the cross-sectional design precludes causal inference regarding whether the observed fungal alterations drive or result from fibrosis. Second, the functional predictions derived from bioinformatics analyses require experimental validation. Third, the moderate sample size may limit the statistical power to detect certain associations. Future investigations should incorporate larger cohorts with longitudinal designs and employ multi-omics approaches (e.g., metabolomics, transcriptomics) to elucidate fungal-host interaction mechanisms. Furthermore, the characteristic fungal signatures identified in this study warrant exploration of targeted microbiome-based interventions, such as antifungal therapies or fungal microbiota transplantation. Conclusion In summary, this study comprehensively characterizes the intestinal fungal microbiota and its predicted metabolic functions in CD patients with intestinal fibrosis. The findings reveal that stricturing patients exhibit a significant reduction in microbial diversity, specific fungal community changes (such as the enrichment of Alternaria ), and pro-fibrotic metabolic features (e.g., upregulation of the LOXL pathway). These findings expand our understanding of microbiome-fibrosis associations and highlight candidate pathways for mechanistic validation. Notably, the LOXL pathway represents a testable hypothesis for linking fungal dysbiosis to fibrotic progression. Future studies should explore whether targeted modulation of gut fungi could influence fibrosis outcomes in CD. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of the First Affiliated Hospital with Nanjing Medical University (Approval No. 2023-SR-149) and prospectively registered with the Chinese Clinical Trial Registry (Registration No. ChiCTR2100054258). All participating subjects provided written informed consent prior to enrollment. The research was conducted in strict accordance with both the ethical guidelines of the approving institution and the principles of the Declaration of Helsinki. Consent for publication Not applicable. Data Availability The datasets generated and analysed during the current study are available in the Genome Sequence Archive of the National Genomics Data Center (https://ngdc.cncb.ac.cn/) under BioProject accession number PRJCA043205 (https://ngdc.cncb.ac.cn/gsa-human/browse/HRA012362) and PRJCA043240 (https://ngdc.cncb.ac.cn/gsa-human/browse/HRA012364). All data can be accessed without restriction through the provided links. Competing interests The authors declare that there is no conflict of interest. Funding National Natural Science Foundation of China [Grant Nos.82200582 and 82370535] and Normal Projects of Innovation Training for College Students in Jiangsu Province [202310312043Z]. Authors’ contributions Junjian Sun: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing- Original draft preparation. Junjie Lin: Investigation, Methodology, Formal analysis. Shu Wang: Methodology, Formal analysis, Software. Jiayun Wang: Methodology, Formal analysis, Software. Lu Wang: Methodology, Formal analysis, Software. Yanqiu Yu: Methodology, Formal analysis, Software. Hongjie Zhang: Conceptualization, Writing - Review & Editing, Supervision, Funding acquisition. Xiaojing Zhao: Conceptualization, Validation, Writing - Original Draft, Writing - Review & Editing, Supervision, Funding acquisition. Acknowledgements Not applicable. References Lin X, Wang Y, Liu Z, et al. Intestinal strictures in Crohn's disease: a 2021 update. Therap Adv Gastroenterol 2022; 15 : 17562848221104951. Yoo JH, Holubar S, Rieder F. Fibrostenotic strictures in Crohn's disease. Intest Res 2020; 18 (4): 379-401. Bettenworth D, Baker ME, Fletcher JG, et al. A global consensus on the definitions, diagnosis and management of fibrostenosing small bowel Crohn's disease in clinical practice. Nat Rev Gastroenterol Hepatol 2024; 21 (8): 572-84. Bettenworth D, Baker ME, Fletcher JG, et al. 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Nat Rev Microbiol 2019; 17 (8): 497-511. Zamara E, Novo E, Marra F, et al. 4-Hydroxynonenal as a selective pro-fibrogenic stimulus for activated human hepatic stellate cells. J Hepatol 2004; 40 (1): 60-8. Catapano A, Cimmino F, Petrella L, et al. Iron metabolism and ferroptosis in health and diseases: The crucial role of mitochondria in metabolically active tissues. J Nutr Biochem 2025; 140 : 109888. Wei S, Gao L, Wu C, Qin F, Yuan J. Role of the lysyl oxidase family in organ development (Review). Exp Ther Med 2020; 20 (1): 163-72. Cox TR, Bird D, Baker AM, et al. LOX-mediated collagen crosslinking is responsible for fibrosis-enhanced metastasis. Cancer Res 2013; 73 (6): 1721-32. de Bruyn JR, van den Brink GR, Steenkamer J, et al. Fibrostenotic Phenotype of Myofibroblasts in Crohn's Disease is Dependent on Tissue Stiffness and Reversed by LOX Inhibition. J Crohns Colitis 2018; 12 (7): 849-59. Schilter H, Findlay AD, Perryman L, et al. The lysyl oxidase like 2/3 enzymatic inhibitor, PXS-5153A, reduces crosslinks and ameliorates fibrosis. J Cell Mol Med 2019; 23 (3): 1759-70. Chen W, Yang A, Jia J, Popov YV, Schuppan D, You H. Lysyl Oxidase (LOX) Family Members: Rationale and Their Potential as Therapeutic Targets for Liver Fibrosis. Hepatology 2020; 72 (2): 729-41. Baliou S, Adamaki M, Ioannou P, et al. Protective role of taurine against oxidative stress (Review). Mol Med Rep 2021; 24 (2). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6966692","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511355176,"identity":"ab3c45b5-d20f-418a-93cc-340c2a670fe2","order_by":0,"name":"Junjian Sun","email":"","orcid":"","institution":"The First Affiliated Hospital with Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junjian","middleName":"","lastName":"Sun","suffix":""},{"id":511355177,"identity":"0a514263-035d-4a80-a8ec-228005ec3392","order_by":1,"name":"Junjie Lin","email":"","orcid":"","institution":"The First Affiliated Hospital with Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Lin","suffix":""},{"id":511355178,"identity":"87f9f76a-2b4d-4a31-b3d9-68c30d4fc86e","order_by":2,"name":"Shu Wang","email":"","orcid":"","institution":"The First Affiliated Hospital with Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shu","middleName":"","lastName":"Wang","suffix":""},{"id":511355179,"identity":"76d52904-b796-4584-be95-355e14d78777","order_by":3,"name":"Jiayun Wang","email":"","orcid":"","institution":"The First Affiliated Hospital with Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiayun","middleName":"","lastName":"Wang","suffix":""},{"id":511355180,"identity":"e81b565e-b4b7-4b16-af34-fc06f9e067dc","order_by":4,"name":"Lu Wang","email":"","orcid":"","institution":"The First Affiliated Hospital with Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Wang","suffix":""},{"id":511355181,"identity":"e7c7303e-7742-45cb-9281-0825a6d038ce","order_by":5,"name":"Yanqiu Yu","email":"","orcid":"","institution":"The Affiliated Jiangning Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanqiu","middleName":"","lastName":"Yu","suffix":""},{"id":511355182,"identity":"1ad00deb-7f8a-49dd-9644-2c0eb7a8fd7a","order_by":6,"name":"Hongjie Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital with Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongjie","middleName":"","lastName":"Zhang","suffix":""},{"id":511355183,"identity":"184f833c-1ce2-4d21-8784-7da02f0c2d44","order_by":7,"name":"Xiaojing Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYFCCA2xAwoaBsQHEYSNeSxpJWsDKDiOxCQGDg8efPfhRcT6PedoZA4YPZYcZ+Gc34Nci2XAg3bDnzO1ixtk5Bowzzh1mkLhzAL8WfoYDxyR4224nNgK1MPO2HWYwkEgg5JGDbZJ/285BtPwlRgs/w2E2ad62AxAtjMRokWw4xiYtcyYZqCWt4GDPuXQeiRsEtBjcOP5M8k2FXeLG2ckbH/wos5bjn0FAC4PEAQht2ACMVSDNQ0A9yDMNEFqesNJRMApGwSgYqQAAPNtF9N2soz0AAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital with Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiaojing","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-06-24 14:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6966692/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6966692/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90899236,"identity":"8f9dd5b2-7248-4da7-9632-cd42373b9714","added_by":"auto","created_at":"2025-09-09 11:58:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAltered fungal diversity and richness between \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eCD patients with and without fibrostenotic strictures\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A)\u003c/em\u003e \u003cem\u003eNumber of fungal amplicon sequence variants (ASVs) in non-stricturing versus stricturing CD groups. (B) \u003c/em\u003ePrincipal coordinate analysis (PCoA) based on unweighted UniFrac distances, revealing significant separation between groups (\u003cem\u003eP\u003c/em\u003e = 0.01). Axes represent the top three principal coordinates (PCoA1–3), with percentages indicating explained variance. (C–E) Alpha diversity metrics: observed species (C), Shannon index (D), and Chao1 index (E).\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6966692/v1/52593064e390e71434bb8ea6.jpg"},{"id":90899234,"identity":"89531237-e00d-4192-a7df-01b3d2618efc","added_by":"auto","created_at":"2025-09-09 11:58:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":116738,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFungal community profiling in CD patients with and without strictures. \u003c/strong\u003eRelative abundance of fungal phyla (A) and genera (B) in stricture versus non-stricture groups. (C) Histogram of the LDA scores (log10) computed for features differentially abundant in the stricture and non-stricture groups. (D) Sankey diagram of dominant fungal communities in CD patients.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6966692/v1/3584768a2ebe0aad8a7ee411.jpg"},{"id":90899238,"identity":"aa8f4579-df31-431f-9311-bbdf2e38d0c9","added_by":"auto","created_at":"2025-09-09 11:58:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68536,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCorrelation analysis between fungal abundance and clinical parameters. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A) \u003c/em\u003eHeatmap of Spearman correlation coefficients between the relative abundances of dominant fungal genera and clinical parameters in Crohn’s disease patients (n = 41).\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP \u0026lt; 0.05\u003c/em\u003e,\u003csup\u003e **\u003c/sup\u003e\u003cem\u003e P \u0026lt; \u003c/em\u003e0.01.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6966692/v1/bd4024dab5658a55a0415589.jpg"},{"id":90899860,"identity":"a4b9770c-3559-40f7-9dd4-52a4aaceb4bd","added_by":"auto","created_at":"2025-09-09 12:06:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":193943,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between Intestinal Fungal and Bacterial Composition. \u003c/strong\u003e(A) Sankey diagram of dominant bacterial communities in CD patients. Inter- and intra-kingdom correlations between intestinal bacteria and fungi at phylum (B) and genus (C) level. \u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e P \u0026lt; 0.05\u003c/em\u003e, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003e P \u0026lt; \u003c/em\u003e0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003e P \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6966692/v1/316b2e41c7447ea649b89abd.jpg"},{"id":90899239,"identity":"1217d7da-8349-4b6f-a9c8-291ab8dcbf5a","added_by":"auto","created_at":"2025-09-09 11:58:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":122900,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential fungal functional profiles in CD patients. \u003c/strong\u003eMetagenomic analysis of fungal functional pathways predicted by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States(PICRUSt2): (A) COG functions, (B) KEGG Level 3 pathways, and (C) MetaCyc pathways. For each panel, the top 30 significantly enriched functions (Wilcoxon test, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) are displayed. Left plots show functional abundance ratios between groups (dots represent effect size with 95% confidence intervals); right values indicate \u003cem\u003eP\u003c/em\u003e-values. Statistical analysis was performed using STAMP v2.1.3.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6966692/v1/7bc0c388d26f2ee9b5d4dd40.jpg"},{"id":101751414,"identity":"927c991b-5069-4d3a-9e32-2b5129afb932","added_by":"auto","created_at":"2026-02-03 10:20:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1580574,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6966692/v1/d6a980f1-a1bf-4bd1-a4e9-f35b75d51a7e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of fungal and bacterial Dysbiosis in Crohn's Disease Patients with Intestinal Fibrosis","fulltext":[{"header":"Background","content":"\u003cp\u003eCrohn’s disease (CD) is a chronic relapsing inflammatory disorder of the gastrointestinal tract. As the disease progresses, the incidence of intestinal strictures gradually increases, with up to 70% of CD patients developing at least one stricture within 10 years of diagnosis, which can lead to intestinal obstruction or even perforation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The pathological basis of intestinal strictures is intestinal fibrosis. Despite the recent clinical application of various biologics, there is currently no evidence that these agents can reverse established fibrosis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Intestinal strictures and their complications, such as perforation, remain major challenges in CD management\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. While the relationship between bacterial communities and CD has been extensively studied, the role of fungi-a critical component of the gut microbiome-in CD, particularly in patients with intestinal fibrosis, remains poorly understood.\u003c/p\u003e\u003cp\u003eIn recent years, the role of gut dysbiosis in CD pathogenesis has garnered significant attention\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, existing research has primarily focused on bacterial communities, with limited investigation into fungal populations. Although fungi represent only 0.1% of the total gut microbiota, they play crucial roles in maintaining intestinal homeostasis through multiple mechanisms\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Structurally, the healthy human gut is primarily colonized by five major fungal phyla, with \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eBasidiomycota\u003c/em\u003e being predominant, along with dominant genera such as \u003cem\u003eCandida\u003c/em\u003e and \u003cem\u003eSaccharomyces\u003c/em\u003e, which exhibit a gradient distribution from the ileum to the colon\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Functionally, fungi contribute to host immunity by stimulating IgA secretion and regulating cytokine networks, participate in metabolic processes such as complex polysaccharide degradation and vitamin synthesis, and engage in cross-kingdom interactions with bacteria to maintain ecological balance in the gut\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNotably, growing evidence suggests that fungal dysbiosis is closely linked to CD pathogenesis\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Our previous study revealed distinct fungal microbiota profiles between the healthy controls and CD groups, with significant differences emerging at the genus level rather than the phylum level. Notably, the \u003cem\u003eCandida\u003c/em\u003e genus was significantly enriched in patients with active CD compared to healthy controls\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Some studies have revealed significantly reduced fungal diversity in CD patients, along with overgrowth of opportunistic pathogens like \u003cem\u003eCandida albicans\u003c/em\u003e and elevated anti-Saccharomyces cerevisiae antibodies (ASCA)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Intriguingly, recent work implicates specific fungi (e.g., \u003cem\u003eMalassezia\u003c/em\u003e) in CD heterogeneity through CARD9-mediated aberrant immune responses, offering new insights into disease mechanisms\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, the role of fungi in CD-associated intestinal fibrosis, particularly their contribution to stricture formation, remains largely unexplored.\u003c/p\u003e\u003cp\u003eTo address this gap, our study focuses on characterizing the gut mycobiota in CD patients with fibrostenotic strictures. By analyzing fecal samples from stricturing and non-stricturing CD patients using high-throughput sequencing and bioinformatics, we aim to identify fungal signatures unique to fibrostenotic CD, elucidate potential links between fungal functional shifts and fibrosis, and explore fungal biomarkers for disease prediction. This work will not only advance understanding of fungal contributions to CD-associated intestinal fibrosis but may also provide novel theoretical foundations for early diagnosis and targeted therapies against intestinal fibrosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003ePatients and surgical samples collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003e This single-center prospective cross-sectional study enrolled 41 CD patients (19 with intestinal fibrotic strictures and 22 without) from the Department of Gastroenterology at Jiangsu Province Hospital between August 2024 and February 2025, collecting fecal samples from all participants for analysis. CD diagnoses were based on the European Crohn's and Colitis Organisation (ECCO) guidelines\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, while intestinal strictures were identified using the 2024 Stricture Treatment and Anti-fibrosis Research (STAR) Consortium criteria\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Exclusion criteria included: age \u0026lt; 18 years, recent colonoscopy within 4 weeks prior to enrollment, and antibiotic use within 3 months before enrollment. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes patient characteristics, including age, sex, Body Mass Index (BMI), Montreal classification, platelet count (PLT), hematocrit (HCT), Albumin (ALB), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), fecal calprotectin (FC), and current medications. This study was conducted in full compliance with the ethical principles outlined in the Declaration of Helsinki. The study was approved by the Ethics Committee of the First Affiliated Hospital with Nanjing Medical University (Approval No. 2023-SR-149), and all participants provided written informed consent prior to enrollment. This study was registered at the Chinese Clinical Trial Registry (Registration number: ChiCTR2100054258).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBasic characteristics of CD patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-stricturing CD\u003c/p\u003e\u003cp\u003e(n = 19)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStricturing CD\u003c/p\u003e\u003cp\u003e(n = 22)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge,y, median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (21–30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (26–37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI,kg/m\u003csup\u003e2\u003c/sup\u003e, median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.20 (19.11–23.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.41 (18.80-24.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMontreal classification, n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA1/ A2/A3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 /17/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 /15/7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL1/L2/L3/L4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10/2/7/0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13/2/6/1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, 10^9/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.42 ± 1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.75 ± 2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT,10^9/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e245.6 (194.50-275.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e253.24 (217.50-286.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHCT, %, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.09 ± 4.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.396 ± 5.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALB, g/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.70 ± 5.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.95 ± 5.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP,mg/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.2 (1.9–13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.5 (2.8–17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESR, mm/h, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.63 (5–22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.36 (5-34.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFC, µg/g, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e433.89 (55.45–875.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e561.24 (210.20-862.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent medication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5-aminosalicylic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAzathioprine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiologics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eComparing Non-stricturing CD (n = 19) with Stricturing CD (n = 22) using Mann–Whitney U-test for skewed distributed continuous variables, independent t-test for normal distributed continuous variables, and Chi-square/Fisher exact test for categorical variables. IQR = Interquartile Range, SD = Standard Deviation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStool Collection and DNA Extraction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFecal samples were homogenized and stored at -80°C for subsequent analysis. DNA extraction was performed using the TianGen Magnetic Bead DNA Extraction Kit (TianGen, China, Catalog #: DP712). Specifically, 250 mg of fecal sample was mixed with 500 µL of buffer SA, 100 µL of buffer SC, and 0.25 g of grinding beads. The mixture was homogenized using a TGrinder H24 tissue homogenizer (OSE-TH-01) at 6 m/s for 30 s, with a 30 s interval, for two cycles, followed by heat lysis at 70°C for 15 min. After centrifugation at 12,000 rpm for 1 min, the supernatant was collected and mixed with 200 µL of buffer SH by vortexing for 5 s, then incubated at 4°C for 10 min. The mixture was centrifuged again at 12,000 rpm for 3 min, and the supernatant was combined with 500 µL of buffer GFA and inverted to mix thoroughly. Magnetic bead suspension G was added, and the mixture was oscillated for 5 min before proceeding with subsequent steps according to the manufacturer's instructions until extraction was complete. DNA concentration was measured using a NanoDrop spectrophotometer (Thermo Fisher Scientific). Additionally, FC levels were determined using the Calpro AS ELISA kit. Samples were diluted at a 1:50 ratio with Calpro EasyExtract and processed according to the manufacturer's protocol.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInternal Transcribed Spacer sequencing (ITS sequencing)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eITS2 region: Forward primer ITS1FI2 (5'-GTGARTCATCGAATCTTTG-3'), reverse primer ITS2 (5'-TCCTCCGCTTATTGATATGC-3'). ITS1 region: Forward primer F (5'-GAACCWGCGGARGGATCA-3'), reverse primer R (5'-GCTGCGTTCTTCATCGATGC-3'). Sample-specific barcodes and universal sequencing adapters were added to the 5' end of each primer. The PCR reaction was performed in a total volume of 25 µL, containing 25 ng of template DNA, 12.5 µL of PCR Premix, 2.5 µL each of forward and reverse primers, and PCR-grade water to adjust the final volume. The ITS amplification protocol was as follows: Initial denaturation at 98°C for 30 sec; 32 cycles of amplification (98°C for 10 sec, 54°C for 30 sec, 72°C for 45 sec); Final extension at 72°C for 10 min. PCR products were verified by 2% agarose gel electrophoresis. Throughout the DNA extraction process, ultrapure water was used as a negative control in place of sample solutions to exclude false-positive results. PCR products were purified using AMPure XT beads (Beckman Coulter Genomics, USA) and quantified with Qubit (Invitrogen, USA). After amplicon library construction, fragment size and concentration were assessed using the Agilent 2100 Bioanalyzer (Agilent, USA) and the Illumina Library Quantification Kit (Kapa Biosciences, USA), respectively. Finally, paired-end sequencing (PE250) was performed on the NovaSeq platform.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003e After paired-end sequencing on the Illumina NovaSeq platform, the raw data were initially demultiplexed based on sample-specific barcodes followed by removal of both barcode and primer sequences. The paired-end reads were then merged using PEAR software, and high-quality clean tags were obtained through stringent quality control with fqtrim (v0.94), with subsequent chimera filtering performed using Vsearch (v2.3.4). DADA2 was utilized to generate amplicon sequence variants (ASVs) for the ITS and 16S sequences for each sample, which were normalized by subsampling to the minimum sequencing depth per sample in QIIME2 for subsequent diversity analyses. Both α-diversity and β-diversity indices were calculated and visualized using R (v4.4.2). For taxonomic classification, the feature-classifier plugin in QIIME2 was employed to annotate sequences against the RDP and UNITE reference databases, with fungal community composition analyzed based on relative abundance (calculated as counts of specific fungi divided by total counts).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eAlterations in gut fungal community diversity characteristics in CD patients with intestinal fibrosis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe findings demonstrate significant alterations in gut fungal community diversity among CD patients with intestinal fibrosis. The Venn diagram showed that 616 ASVs were generated from non-stricturing samples and 307 ASVs were generated from the structuring samples, with 125 ASVs shared between both groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). β-diversity analysis based on PERMANOVA testing of Unweighted UniFrac distances demonstrated significant intergroup differences (R\u0026sup2;=0.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), and Principal Coordinate Analysis (PCoA) confirmed distinct clustering patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Assessment using three α-diversity indices (observed species, Shannon diversity index, and Chao1 richness index) revealed decreased trends across all metrics in the stricture group, with the Chao1 index showing statistically significant reduction (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating diminished species richness associated with stricture formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-E). These findings suggest that stricture patients exhibit a characteristic \u0026ldquo;diversity depletion\u0026rdquo; pattern in their fungal communities, reflecting a transition from complex and stable to simplified and vulnerable ecosystem structures. From a pathophysiological perspective, the loss of these fungal populations could hypothetically impair immune regulation via the \u0026ldquo;microbiota-immune-fibrosis\u0026rdquo; axis, potentially contributing to disease progression\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAlterations in fungal microbiota composition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur study revealed that \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eBasidiomycota\u003c/em\u003e constituted the predominant fungal phyla in the gut microbiota, with \u003cem\u003eAscomycota\u003c/em\u003e being the most abundant in both the stricture (70.45%) and non-stricture groups (67.78%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). Notably, the stricture group exhibited a significantly higher \u003cem\u003eBasidiomycota/Ascomycota\u003c/em\u003e ratio (0.32 vs. 0.27). LEfSe analysis identified significant enrichment of \u003cem\u003eAlternaria\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) in the stricture group, alongside marked reductions in \u003cem\u003eEurotium\u003c/em\u003e and \u003cem\u003eGuehomyces\u003c/em\u003e (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Although not statistically significant, trends of increased relative abundance were observed for \u003cem\u003eMalassezia, Meyerozyma, Hypocrea, Candida\u003c/em\u003e, and \u003cem\u003ePenicillium\u003c/em\u003e in stricture patients, whereas \u003cem\u003eSaccharomyces, Cryptococcus\u003c/em\u003e, and \u003cem\u003eEupenicillium\u003c/em\u003e showed decreased abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The observed alterations in specific fungal taxa could potentially relate to intestinal fibrotic progression, but this preliminary association warrants further mechanistic validation. The enrichment of \u003cem\u003eAlternaria\u003c/em\u003e in stricture patients warrants further investigation as a possible microbial signature associated with stricture status, though its utility as a clinical biomarker would require validation in independent cohorts.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation analysis between gut fungal taxa and clinical parameters\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSpearman\u0026rsquo;s rank correlation analysis revealed significant associations between specific gut fungal taxa and clinical indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Notably, the relative abundances of \u003cem\u003eAlternaria\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e = -0.32, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) and \u003cem\u003ePenicillium\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e = -0.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) demonstrated significant negative correlations with WBC, while \u003cem\u003eEurotium\u003c/em\u003e showed a significant positive correlation (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.39, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). Furthermore, the abundances of \u003cem\u003eMalassezia\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.39, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) and \u003cem\u003eCryptococcus\u003c/em\u003e (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) were positively associated with PLT. These findings reveal significant correlations between specific gut fungal taxa and systemic inflammatory markers, implying a potential link between gut fungal community variations and inflammatory responses in CD patients with intestinal fibrosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation between Intestinal Fungal and Bacterial Composition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe further observed changes in bacterial composition in CD patients with (n\u0026thinsp;=\u0026thinsp;22) and without (n\u0026thinsp;=\u0026thinsp;19) intestinal fibrosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). We systematically evaluated the interkingdom relationships between fungal and bacterial taxa at both phylum and genus levels in stricturing versus non-stricturing phenotypes. At the phylum level, \u003cem\u003eAscomycota\u003c/em\u003e showed a negative correlation with \u003cem\u003eBasidiomycota\u003c/em\u003e and \u003cem\u003eZygomycota\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). \u003cem\u003eActinobacteriota\u003c/em\u003e was positively correlated with \u003cem\u003eAscomycota\u003c/em\u003e and negatively correlated with \u003cem\u003eBasidiomycota\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). At the genus level, we observed a relationship between the 27 main bacteria (found in more than 50% of the samples and had a relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;0.1%) and 8 main fungal genera. The complex relationships between bacteria and fungi are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional prediction analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate functional differences in fungal communities between stricturing and non-stricturing CD patients, we conducted systematic functional prediction\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and comparative analyses based on the COG database (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The results revealed distinct metabolic signatures in the fibrostenotic group, with significant enrichment of genes encoding key metabolic enzymes, including acetyl-CoA carboxylase and NADPH-dependent ferric reductase (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that fungal metabolic differences, particularly in lipid metabolism and oxidative stress responses, may be relevant to intestinal fibrosis. Conversely, the non-stricturing group exhibited higher abundance of chitinase genes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), potentially reflecting distinct ecological functions. While these predictions highlight metabolic disparities, their direct relevance to fibrosis requires experimental validation.\u003c/p\u003e\u003cp\u003eKEGG-based functional prediction further identified fibrosis-related pathways significantly upregulated in stricturing patients, including lysyl oxidase-like proteins 2/3/4 (LOXL2/3/4)-key enzymes mediating ECM crosslinking-methylmalonyl-CoA transferase (involved in short-chain fatty acid metabolism), and ABC-type iron transport systems (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). These predicted functional differences suggest possible microbial contributions to fibrosis through ECM remodeling, metabolic and iron homeostasis pathways. Intriguingly, elevated chitinase in non-stricturing patients may indicate distinct host-microbe interactions. Notably, previous studies implicate circulating LOXL2 in ECM deposition\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, warranting investigation of its biomarker potential for fibrosis monitoring. However, the therapeutic implications of these predicted pathways require experimental validation.\u003c/p\u003e\u003cp\u003eMetaCyc pathway analysis further highlighted metabolic reprogramming in stricturing patients: while non-stricturing samples showed enrichment in tryptophan/branched-chain amino acid biosynthesis (e.g., L-isoleucine synthesis) and fatty acid β-oxidation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating robust nutrient metabolism, stricturing samples exhibited upregulated cell wall biosynthesis (e.g., UDP-N-acetylmuramate pathway) and lipid metabolism (e.g., palmitoleic acid synthesis), alongside downregulated taurocholate degradation-a pathway linked to mucosal protection (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). This metabolic shift is consistent with a potential pro-inflammatory state and impaired barrier integrity, though direct evidence linking these pathways to inflammation or barrier dysfunction requires further validation. These findings highlight distinct microbial metabolic patterns associated with intestinal strictures, expanding our understanding of potential microbiome-metabolite interactions in fibrosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study provides a comprehensive analysis of the intestinal fungal community and its predicted metabolic functions in CD patients with intestinal fibrosis. We observed that stenotic CD patients exhibit significant fungal dysbiosis, characterized by altered community structure and enriched metabolic pathways (e.g., ECM remodeling, lipid metabolism). While these findings suggest possible links between fungal ecology and fibrotic progression, further mechanistic studies are needed to establish causal relationships. This work lays the foundation for future investigations into fungal-host interactions in fibrosis.\u003c/p\u003e\u003cp\u003eOur study found that patients in the stenosis group exhibited a significant reduction in fungal α-diversity, particularly with the Chao1 index indicating a decrease in species richness, which aligns with previous reports of diminished microbial diversity in inflammatory bowel disease\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Notably, β-diversity analysis revealed distinct fungal community structures between stenotic and non-stenotic patients, likely reflecting microbial selection pressures specific to fibrotic progression. Reduced community diversity, often considered a marker of ecosystem instability, may lead to a \u0026ldquo;diversity collapse\u0026rdquo; in the gut microenvironment, resulting in the loss of keystone fungal species and compromised microbial resilience\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. From a pathophysiological standpoint, the observed reduction in fungal diversity may contribute to fibrotic progression through several interconnected mechanisms. The diminished presence of beneficial fungi such as \u003cem\u003eSaccharomyces boulardii\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e compromises their crucial anti-inflammatory and mucosal protective functions, while the disrupted equilibrium in fungal-bacterial crosstalk induces microenvironmental alterations\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Furthermore, the dysregulation of critical fungal-mediated metabolic networks, especially those governing immunomodulatory pathways, may create a permissive milieu for fibrosis development.\u003c/p\u003e\u003cp\u003eAt the community composition level, our study revealed an elevated \u003cem\u003eBasidiomycota/Ascomycota\u003c/em\u003e ratio in stenosis patients, partially consistent with previous findings regarding fungal community alterations in IBD\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Particularly noteworthy was the significant enrichment of \u003cem\u003eAlternaria\u003c/em\u003e in the stenosis group, a finding with important clinical implications. As a common environmental saprophytic fungus, certain \u003cem\u003eAlternaria\u003c/em\u003e species are known to produce allergenic proteins and mycotoxins, with established associations to respiratory allergic diseases\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Our study reports novel observations of Alternaria enrichment in CD-associated intestinal stenosis in our cohort. Notably, we observed an inverse correlation between \u003cem\u003eAlternaria\u003c/em\u003e abundance and systemic inflammatory markers (WBC count). While the exact mechanisms remain speculative, previous studies suggest several plausible explanations that warrant further investigation. \u003cem\u003eAlternaria\u003c/em\u003e mycotoxins have demonstrated epithelial-damaging effects in vitro\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e; certain fungal components are known to elicit Th2-skewed immune responses\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e; and microbial metabolites can influence fibroblast activation in other systems\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, the functional consequences of \u003cem\u003eAlternaria\u003c/em\u003e enrichment in intestinal fibrosis require direct experimental validation.\u003c/p\u003e\u003cp\u003eThe observed correlations between intestinal fungal and bacterial taxa in CD patients with or without fibrosis suggest potential microbial interactions influencing disease progression. The negative correlation between \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eBasidiomycota/Zygomycota\u003c/em\u003e at the phylum level may indicate competitive ecological niches, while the positive association between \u003cem\u003eActinobacteriota\u003c/em\u003e and \u003cem\u003eAscomycota\u003c/em\u003e could imply synergistic metabolic or immunomodulatory effects. The complex bacterial-fungal network at the genus level highlights the need for further mechanistic studies to elucidate whether these microbial shifts contribute to fibrotic complications or merely reflect disease severity\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Understanding these interactions may open new therapeutic avenues targeting the gut mycobiome-bacteriome axis in CD.\u003c/p\u003e\u003cp\u003eFunctional prediction analysis yielded deeper insights into the pathological roles of fungal communities\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The enrichment of metabolic enzymes such as acetyl-CoA carboxylase and NADPH-dependent ferric reductase in the stenosis group suggests that lipid metabolism and oxidative stress may play pivotal roles in fibrogenesis. As the rate-limiting enzyme in fatty acid synthesis, upregulated acetyl-CoA carboxylase could lead to lipid accumulation, while lipid peroxidation products like 4-hydroxynonenal (4-HNE) have been shown to activate myofibroblasts\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. NADPH-dependent ferric reductase participates in iron metabolism, and iron overload may promote reactive oxygen species generation, exacerbating tissue damage and fibrosis\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. KEGG analysis revealed particularly clinically significant enrichment of LOXL2/3/4 pathways. LOX family enzymes serve as crucial mediators of ECM crosslinking, directly involved in collagen fiber maturation and stabilization\u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Our findings suggest that gut fungi may promote ECM deposition by upregulating LOX expression, providing novel perspectives for anti-fibrotic strategy development. Notably, recent studies have demonstrated the efficacy of LOXL2 inhibitors (e.g., PXS-5153A) in hepatic fibrosis models\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, and our results provide a theoretical foundation for extending such therapeutics to intestinal fibrosis.\u003c/p\u003e\u003cp\u003eMetaCyc pathway analysis revealed distinct metabolic reprogramming features in stenosis patients. The upregulation of cell wall component biosynthesis pathways likely reflects fungal adaptation to the pro-inflammatory environment through enhanced protective mechanisms, while downregulation of taurine degradation pathways may compromise its anti-inflammatory and antioxidant effects. As a compound with demonstrated membrane-stabilizing, calcium homeostasis-regulating, and free radical-scavenging properties, reduced taurine metabolism could exacerbate oxidative damage and inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. These findings collectively delineate a \u0026ldquo;pro-fibrotic\u0026rdquo; metabolic landscape, offering novel insights into how microbial metabolism influences host pathological processes.\u003c/p\u003e\u003cp\u003eWhile this study provides preliminary insights into the fungal microbiota characteristics in CD-related intestinal fibrosis, several limitations should be addressed. First, the cross-sectional design precludes causal inference regarding whether the observed fungal alterations drive or result from fibrosis. Second, the functional predictions derived from bioinformatics analyses require experimental validation. Third, the moderate sample size may limit the statistical power to detect certain associations. Future investigations should incorporate larger cohorts with longitudinal designs and employ multi-omics approaches (e.g., metabolomics, transcriptomics) to elucidate fungal-host interaction mechanisms. Furthermore, the characteristic fungal signatures identified in this study warrant exploration of targeted microbiome-based interventions, such as antifungal therapies or fungal microbiota transplantation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study comprehensively characterizes the intestinal fungal microbiota and its predicted metabolic functions in CD patients with intestinal fibrosis. The findings reveal that stricturing patients exhibit a significant reduction in microbial diversity, specific fungal community changes (such as the enrichment of \u003cem\u003eAlternaria\u003c/em\u003e), and pro-fibrotic metabolic features (e.g., upregulation of the LOXL pathway). These findings expand our understanding of microbiome-fibrosis associations and highlight candidate pathways for mechanistic validation. Notably, the LOXL pathway represents a testable hypothesis for linking fungal dysbiosis to fibrotic progression. Future studies should explore whether targeted modulation of gut fungi could influence fibrosis outcomes in CD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of the First Affiliated Hospital with Nanjing Medical University (Approval No. 2023-SR-149) and prospectively registered with the Chinese Clinical Trial Registry (Registration No. ChiCTR2100054258). All participating subjects provided written informed consent prior to enrollment. The research was conducted in strict accordance with both the ethical guidelines of the approving institution and the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe datasets generated and analysed during the current study are available in the Genome Sequence Archive of the National Genomics Data Center (https://ngdc.cncb.ac.cn/) under BioProject accession number PRJCA043205 (https://ngdc.cncb.ac.cn/gsa-human/browse/HRA012362) and PRJCA043240 (https://ngdc.cncb.ac.cn/gsa-human/browse/HRA012364). All data can be accessed without restriction through the provided links.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China [Grant Nos.82200582 and 82370535] and Normal Projects of Innovation Training for College Students in Jiangsu Province [202310312043Z].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJunjian Sun: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing- Original draft preparation. Junjie Lin: Investigation, Methodology, Formal analysis.\u003csup\u003e \u003c/sup\u003eShu Wang: Methodology, Formal analysis, Software. Jiayun Wang: Methodology, Formal analysis, Software. Lu Wang: Methodology, Formal analysis, Software. Yanqiu Yu: Methodology, Formal analysis, Software. Hongjie Zhang: Conceptualization, Writing - Review \u0026amp; Editing, Supervision, Funding acquisition.\u003csup\u003e \u003c/sup\u003eXiaojing Zhao: Conceptualization, Validation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Supervision, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLin X, Wang Y, Liu Z, et al. Intestinal strictures in Crohn\u0026apos;s disease: a 2021 update. \u003cem\u003eTherap Adv Gastroenterol\u003c/em\u003e 2022; \u003cstrong\u003e15\u003c/strong\u003e: 17562848221104951.\u003c/li\u003e\n\u003cli\u003eYoo JH, Holubar S, Rieder F. 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Protective role of taurine against oxidative stress (Review). \u003cem\u003eMol Med Rep\u003c/em\u003e 2021; \u003cstrong\u003e24\u003c/strong\u003e(2).\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":"Intestinal Fungi, Crohn’s disease, Intestinal fibrosis","lastPublishedDoi":"10.21203/rs.3.rs-6966692/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6966692/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIntestinal fibrosis is a serious complication of Crohn's disease (CD) that often leads to strictures and surgery. Although the bacterial microbiome's role in CD pathogenesis has been extensively characterized, the fungal microbiota's contribution to fibrotic progression remains poorly defined. Growing evidence suggests fungi may influence fibrosis through immune and metabolic pathways. This study systematically evaluated compositional and functional alterations in the gut mycobiota associated with CD-related intestinal fibrosis.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eFecal samples from well-characterized CD patients with (n\u0026thinsp;=\u0026thinsp;22) and without (n\u0026thinsp;=\u0026thinsp;19) intestinal fibrosis underwent ITS and 16S rRNA gene sequencing (Illumina MiSeq platform, V4 region). Bioinformatics analysis included: (1) α-diversity assessment; (2) β-diversity evaluation via unweighted UniFrac distances with PERMANOVA; (3) differential abundance analysis using LEfSe (LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2.0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); and (4) Spearman's rank correlation for fungal taxa-clinical parameter associations. Functional profiling was performed through phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt2) with COG, KEGG, and MetaCyc databases.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn this study, fecal samples from well-characterized CD patients with (n\u0026thinsp;=\u0026thinsp;22) and without (n\u0026thinsp;=\u0026thinsp;19) intestinal fibrosis underwent ITS and 16S rRNA gene sequencing. CD patients with intestinal fibrosis demonstrated significant alterations in gut fungal ecology, characterized by reduced α-diversity (Chao1 index, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and distinct β-diversity clustering (PERMANOVA, R\u0026sup2;=0.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). The stricturing group showed marked enrichment of \u003cem\u003eAlternaria\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) and an increased \u003cem\u003eBasidiomycota/Ascomycota\u003c/em\u003e ratio, suggesting phylum-level shifts in fungal composition. Notably, \u003cem\u003eAlternaria\u003c/em\u003e and \u003cem\u003ePenicillium\u003c/em\u003e abundances exhibited significant negative correlations with systemic inflammatory markers (WBC counts, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This study also found various interactions between intestinal fungi and bacteria. Functional analyses revealed concurrent upregulation of pro-fibrotic pathways including LOXL-mediated extracellular matrix remodeling and lipid metabolism, alongside impaired protective functions evidenced by suppressed taurocholate degradation (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study reveals gut fungal dysbiosis with specific taxonomic and functional shifts in CD-associated fibrosis, highlighting \u003cem\u003eAlternaria\u003c/em\u003e enrichment and LOXL-mediated ECM remodeling as potential therapeutic targets. These findings provide new insights into microbial contributions to intestinal fibrogenesis. (Chinese Clinical Trial Registry Center, ChiCTR2100054258, Registered 12 December 2021)\u003c/p\u003e","manuscriptTitle":"Characterization of fungal and bacterial Dysbiosis in Crohn's Disease Patients with Intestinal Fibrosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 11:58:51","doi":"10.21203/rs.3.rs-6966692/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":"8963d8fa-2a87-4d55-b6aa-71870adadf0a","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T10:42:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 11:58:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6966692","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6966692","identity":"rs-6966692","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}