Anti‐Inflammatory Action and Molecular Mechanism of Fucoidan Against Cystitis Glandularis

Anti-Inflammatory Action and Molecular Mechanism of Fucoidan Against Cystitis Glandularis Funding: This study was partly funded by the National Natural Science Foundation of China (No. 82060740). Qingting Chen and Jie Mo contributed equally to this work. ABSTRACT Cystitis glandularis (CG), known as a pre-gradual lesion in the bladder, is the pathological changes in the vesical mucosa characterized by inflammatory invasion and chronic obstruction. Clinically, effective treatment against CG is prescribed only when using drug therapy. Fucoidan, the naturally extractive polysaccharide, is well-reported bioactive compound with anti-inflammatory and immunoregulatory properties. In this research, an emerging computational approach was applied to explicate anti-CG actions and pharmacological targets exhibited by fucoidan in detail. Current network pharmacology data showed that 16 intersection genes of fucoidan and CG were identified, whereas all 6 core targets, including interleukin-6 (IL-6), tumor necrosis factor (TNF), interleukin-1B (IL-1B), matrix metalloproteinase-9 (MMP-9), interleukin-10 (IL-10), matrix metalloproteinase-2 (MMP-2), biological processes, and signaling pathways of fucoidan against CG were characterized, respectively. As revealed in the underlying mechanism, the anti-CG actions achieved by fucoidan were chiefly implicated in the reduction of inflammatory reactions and enhancement of immunoregulation. Taken together, these network bioinformatics findings may be used to reveal anti-CG effects and the pharmacological mechanism of fucoidan before further experimental validation. Furthermore, those core genes identified may be therapeutic targets for research and development of fucoidan-anti-CG. 1 Introduction CG, detected with the lesions including cyst formation, epithelial hyperplasia, glandular metaplasia of goblet cells, and urothelium, is a relatively rare non-neoplastic inflammatory disease (Abdel Magied, Badreldin, and Leslie 2024). Medically, it is speculated controversially that CG may be a possible precursor of cancer development despite clinical evidence being limited (Yi et al. 2014). Nevertheless, CG should be treated by using clinical regimes, as CG may induce sharp pain, odynuria, and vesicovaginal fistulas for severely threatening patients’ health (Ronghua et al. 2024). In medical diagnosis, CG patients are histopathologically identified and accompanied with ultrasound, computed tomography (CT), and conventional magnetic resonance imaging (MRI) tests (Wang et al. 2016). Currently existing treatment, the clinical regimes can have transurethral resection and postoperative intravesical chemotherapy, including cyclooxygenase inhibitors (Bai, Chen, and Zeng 2023). However, clinical medication treating CG is limitedly to be prescribed as it is a refractory disease characterized with poor therapeutic effectiveness and significant recurrence (Takizawa et al. 2016). Thus, specially screening natural ingredients and identifying the pharmacological activities of them may be a promising strategy against CG. Marine algae can be used for extracting anti-inflammatory compounds, including flavonoids, peptides, and polysaccharides (Ghallab et al. 2024). In preclinical evaluation, marine algae may be used for the cytoprotection of hemorrhagic cystitis, such as spirulina (Sinanoglu et al. 2012). Fucus, a brown algae with antioxidant effects, is found with potential anti-inflammatory pharmacological properties (Catarino, Silva, and Cardoso 2018). Fucoidan, a bioactive compound rich in Fucus algae, refers to a sulfuric acid polysaccharide complex that exerts antioxidant, anti-inflammatory, antithrombotic, anticoagulant, antitumor, antiviral actions, and immunoregulation (Zhang et al. 2022). An animal study shows that fucoidan may reduce inflammatory stress in ifosfamide-induced hemorrhagic cystitis (Dornelas-Filho et al. 2018). However, the potent pharmacological action and biotarget of fucoidan against CG need to be evaluated and identified. It is increasingly reported that network pharmacology-based bioinformatics analysis can be used to systematically reveal potential targets and therapeutic mechanisms of bioactive components against clinical disorders (Li et al. 2021; Noor et al. 2022). In addition, our preceding studies highlight that bioinformatics findings by using a network pharmacology approach can be applied for revealing calycosin against osteosarcoma (Pan et al. 2021) and plumbagin against liver cancer (Zhou et al. 2019). In present research, we explained the biological targets and molecular mechanisms of fucoidan action against CG through network pharmacology analysis before future experimental validation, and clinical trials. 2 Material and Methods 2.1 Acquiring the Fucoidan- and CG-Associated Targets Methodologically, the bioactive genes of fucoidan were screened and determined in the Comparative Toxicogenomics Database (https://ctdbase.org) after gene symbol annotation by using the Uniprot Knowledgebase (https://www.uniprot.org). Whereafter, GeneCard Suite (www.genecards.org) was employed to screen CG-associated genes, and the gene set was established by using database retrieval results. All fucoidan- and CG-related gene sets were integrated for identifying intersection targets between fucoidan and CG through Venn diagram analysis (http://bioinformatics.psb.ugent.be/webtools/Venn). 2.2 Determining the Protein–Protein Interaction (PPI) Network and Core Targets All fucoidan- and CG-related gene data sets were employed to construct a PPI network by utilizing the String database (https://string-db.org/), in which the parameter was set as the minimum required interaction score (0.700). The NetworkAnalyzer analysis from Cytoscape software (https://cytoscape.org/) aimed to check and identify the core targets of fucoidan against CG via the topological arithmetic method, as described elsewhere (Kohl, Wiese, and Warscheid 2011). 2.3 Enrichment Analysis for Functional Determination All target gene sets in core targets were used to produce a compound-target network by using R programming language packages, including “ClusterProfiler,” “org.Hs.eg.Db,” “GOplot.” Enrichment analyses for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted to uncover the pharmacological mechanisms, detailed in biological processes, cellular components, molecular functions, and signaling pathways. The “org.Hs.eg.Db” package was used for gene annotation, and gene enrichment with a p value cutoff (0.05) and a q value cutoff (0.05) was employed to plot the bubble and circle charts, as described elsewhere (Lu et al. 2023). 2.4 Creating the Integrated Bioinformatics Graph By utilizing Cytoscape software (Version 3.7.1), the drug-target-gene-ontology-pathway-disease visualization graph was constructed in detail based on the biological process and pathway enrichment findings of fucoidan against CG. 3 Results 3.1 Candidate and Mutual Targets of Fucoidan and CG Being excluded from the duplication data, a gene set of 27 fucoidan-related targets was gained, and a total of 290 target genes of CG were obtained accordingly. Moreover, we summarized 16 intersection gene sets between fucoidan and CG that were harvested finally, in which a fucoidan-CG network diagram interconnecting these intersection genes is visualized in Figure 1. 3.2 Core Targets of Fucoidan Against CG Following the topological parameter algorithm, the median of degrees of freedom was set to 6.627, and the maximum degree of freedom was set to 11. Then, the screening criterion range for core targets was set to from 7 to 11. As a result, all core targets of fucoidan against CG were determined, including IL-6, TNF, IL-1B, MMP-9, IL-10, and MMP-2 (Figure 2). Other detailed information for core targets is presented in Table S1. 3.3 GO-Based Enrichment Analysis Findings Gene ontology enrichment analysis aimed to identify all biological processes (BPs), cellular components (CCs), and molecular functions (MFs) of these core targets. By using adjusted p value < 0.05 and q value < 0.05, those 993 GO-enriched terms were harvested (detailed in Table S2). The top 20 immunologic function–related terms were screened and highlighted in Figure 3, indicating that core targets exerted important action in immunoregulation for managing CG. Meanwhile, the top 20 GO terms associated with inflammation are shown in Figure 4, manifesting that core targets mediated a potential role in regulation of inflammatory stress for managing CG. 3.4 KEGG-Based Enrichment Analysis Findings KEGG enrichment analysis aimed to reveal the pharmacological mechanisms of fucoidan against CG. By utilizing adjusted p value < 0.05 and q value < 0.05, a total of 65 signaling pathways were enriched and identified accordingly, as revealed in Table S3. In detail, these anti-CG molecular pathways were mainly involved in bladder cancer, inflammatory bowel disease, IL-17 signaling pathway, Th17 cell differentiation, T-cell receptor signaling pathway, intestinal immune network for IgA production, systemic lupus erythematosus, C-type lectin receptor signaling pathway, TNF signaling pathway, cytokine–cytokine receptor interaction, Toll-like receptor signaling pathway, nucleotide-binding and oligomerization domain (NOD)-like receptor signaling pathway, cytosolic DNA-sensing pathway, nuclear factor kappa B (NF-κB) signaling pathway, relaxin signaling pathway, Forkhead box O (FoxO) signaling pathway, estrogen signaling pathway, Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway, and mitogen-activated protein kinase (MAPK) signaling pathway (Figure 5). 3.5 Integrative Bioinformatics Findings Furthermore, all these bioinformatics data in this research were integrated for correlative visualization. Interestingly, an integrative network map including fucoidan-target-GO-KEGG-CG connections is plotted and detailed in Figure 6. 4 Discussion By utilizing integrated network pharmacology analysis in this study, we identified all 16 intersection genes of fucoidan and CG, and a total of 6 core targets of fucoidan against CG were screened and ascertained. GO and KEGG enrichment analysis data uncovered that fucoidan exerted anti-CG actions might be related to mainly modulating the biological processes of inflammatory stress, immunologic reaction, and immunoinfiltration, including the IL-17 signaling pathway, Th17 cell differentiation, T-cell receptor signaling pathway, TNF signaling pathway, cytokine–cytokine receptor interaction, Toll-like receptor signaling pathway, and NF-kappa B signaling pathway. These network pharmacology-based findings could pharmacologically reveal the anti-CG potentials of fucoidan predominantly through inflammation-suppressing, immunoinfiltration-reducing, and immunity-enhancing actions. To exhibit more details, we aimed to explicate core targets in fucoidan against CG, as these genes might be the fucoidan-anti-CG pharmacological targets after experimental and clinical validation. IL-6, a keystone cytokine, can play a broad action on the immune system, and cytokine storm symptoms based on its pro-inflammatory property (Barrett 2024). Mounting studies indicate that IL-6 plays a key role in modulating the homeostasis between regulatory T cells and Th17 cells, thus suppression of IL-6 expression contributing to the therapy of inflammatory disorders (Kimura and Kishimoto 2010). It is clinically reported that elevated IL-6 expressions were found in blood and tissue samples in CG patients, suggesting that IL-6 may be a potential target for CG treatment (Qu et al. 2018). TNF, a pleiotropic factor, may regulate the functions of the immune system, cell survival, cell proliferation, and metabolism (Varfolomeev and Vucic 2018). TNF can induce the activation of effector T cells for proliferation, while cell apoptosis in high effector T-cell activity is found by TNF action (Mehta, Gracias, and Croft 2018). As showed in clinical observation, TNF-associated apoptosis-triggering ligand may be responsible for the pathogenesis of interstitial cystitis (Kutlu et al. 2010). IL-1B, a crucial mediator of inflammatory stress, can aggravate tissue impairment when chronic disease or acute injury persistently occurs (Lopez-Castejon and Brough 2011). Cyclophosphamide-induced severe hemorrhagic cystitis is detected with significantly elevated IL-1B cytokine expression in vivo (Mousa et al. 2022). MMP-9 may be involved in immune and inflammation responses because it can activate different cytokines and chemokines (Vafadari, Salamian, and Kaczmarek 2016). MMP-9 may modulate the biological response to inflammatory stress after tissue damage (Wang et al. 2013). Notably, increased urinary contents of MMP-9 and neutrophil gelatinase-associated lipocalin in children with acute cystitis were reported clinically (Hatipoglu et al. 2011). IL-10, an anti-inflammatory cytokine, exerts an important role in regulating inflammatory infiltration and controlling adaptive immune responses that can induce tissue injury (Ouyang and O'Garra 2019). It is found that IL-10 exhibits inhibition of antibody response in bladder impairment during infection (Choi and Abraham 2016). MMP-2 is proven with the cytokine secretion and inflammatory process through being activated functionally (Ribeiro Vitorino et al. 2023). MMP-2 is involved in the epigenetic onset of chronic cystitis in vivo via DNA methylation changes (Choi et al. 2013). These reference reports suggest that reduction of inflammatory response, cytokine infiltration, and enhancement of immune capability of fucoidan against CG are achieved. However, current limitations have been found in this bioinformatics study. Molecular docking imitation analysis should be determined for revealing the binding features between fucoidan and core target proteins in CG. Furthermore, further experimental or clinical validation needs to be performed for determining the pharmacological activities of fucoidan action to treat CG. 5 Conclusion Collectively, current bioinformatics findings from this research highlight the anti-CG potentials of fucoidan, characterizing detailed core targets and molecular mechanisms of fucoidan against CG prior to further validation. These results primarily exhibit that fucoidan may be a beneficial candidate for treating CG. Author Contributions Qingting Chen: conceptualization (equal), data curation (equal), formal analysis (equal), methodology (equal), resources (equal). Jie Mo: data curation (equal), methodology (equal), resources (equal), software (equal), visualization (equal). Yu Li: conceptualization (equal), methodology (equal), resources (equal), software (equal), validation (equal), visualization (equal). Li Gao: investigation (equal), methodology (equal), project administration (equal), supervision (equal), validation (equal), writing – original draft (equal). Ka Wu: funding acquisition (equal), investigation (equal), project administration (equal), supervision (equal), writing – original draft (equal), writing – review and editing (equal). Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study are available upon request from the corresponding author.