Investigating Urine Microbiota and Metabolites in Female Patients with Cystitis Glandularis: A Comprehensive Analysis Using High-Throughput 16S rRNA Sequencing and Metabolomics

Investigating Urine Microbiota and Metabolites in Female Patients with Cystitis Glandularis: A Comprehensive Analysis Using High-Throughput 16S rRNA Sequencing and Metabolomics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Investigating Urine Microbiota and Metabolites in Female Patients with Cystitis Glandularis: A Comprehensive Analysis Using High-Throughput 16S rRNA Sequencing and Metabolomics Yan Chen#, Teng Li, Pengchao Xu, Jinjin Feng, Xuepei Zhang, Katariina Nurmi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7543157/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 Cystitis Glandularis (CG) is a condition that negatively impacts the quality of life in women, often resulting in recurrent urinary tract infections and bladder dysfunction. Existing antibiotic therapies and symptom-focused transurethral resection have proven inadequate. This study aimed to investigate the urinary microbiota diversity and metabolic complexity in female patients with CG before and after treatment, using high-throughput 16S rRNA sequencing and metabolomics analysis. The results indicated a significant reduction in beneficial genera such as Gardnerella, Lactobacillus, and Prevotella in CG patients compared to healthy controls, while an increase was observed in pathogenic taxa such as Acinetobacter, Bacteroides, Enterococcus, Vibrio, and Escherichia-Shigella. Moreover, following antibiotic treatment, a notable decrease in Escherichia-Shigella was observed, along with a slight reduction in Acinetobacter and Bacteroides; however, the abundance of Enterococcus and Vibrio remained unchanged. Additionally, antibiotic treatment correlated with an increase in Ralstonia and Staphylococcus. Metabolomic profiling revealed that 15 out of the top 20 differential metabolites were significantly increased in the treatment group compared to pre-treatment levels. Correlation analyses showed that bacteria associated with healthy controls were positively linked with metabolites such as ephedrine and N-acetylhistidine. In contrast, treatment-associated bacteria, Staphylococcus and Vibrio, exhibited opposite correlations. These findings suggest that current antibiotic treatments are insufficient in restoring microbial equilibrium, potentially exacerbating microbial dysbiosis and metabolic imbalances, thereby contributing to suboptimal outcomes in CG management. This highlights the need for alternative therapeutic strategies to maintain microbial health and enhance treatment efficacy. Health sciences/Diseases Biological sciences/Microbiology Glandular Cystitis Microbiota Metabolites Urine Correlation Analysis of microbiota and metabolites Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Cystitis glandularis (CG) is a condition that originates from recurrent urinary tract infections (UTIs) and bladder dysfunction. It profoundly impacts patients' quality of life and imposes a significant economic burden on healthcare systems. This economic burden is largely due to the high costs associated with managing the condition, as documented in various studies [1,2]. Current treatment protocols primarily focus on symptomatic management, including antibiotic therapy and transurethral resection of bladder lesions [3,4]. However, the effectiveness of these treatments is increasingly compromised by the rising prevalence of antibiotic resistance and the adverse side effects associated with these therapies [4,5,6]. Consequently, there is an urgent need for innovative management strategies and novel treatment approaches to address this challenging condition. In recent years, scientific investigations have highlighted the crucial role of the urinary microbiome in maintaining bladder health and its involvement in disease processes [7,8]. Understanding these findings provides important context for exploring urinary diseases. Specifically, notable differences exist in the composition of the urinary microbiome between healthy individuals and those with lower urinary tract diseases. For instance, patients suffering from interstitial cystitis exhibit reduced diversity in their urinary microbiome along with an increased relative abundance of Lactobacillus [9]. Similarly, individuals with an overactive bladder show decreased microbiome diversity and an elevated abundance of specific bacteria, such as Gardnerella [10]. In neurogenic bladder patients, a higher proportion of bacteria like Enterococcus and Proteus is present in their urine [11]. The metabolome, comprising a collection of small molecules integral to organism metabolism, plays a vital role in maintaining normal growth and function [12]. Metabolomic analysis involves a comprehensive qualitative and quantitative examination of all metabolites present in biological samples. This analysis aims to identify metabolites that show significant differences and biological relevance, thereby revealing underlying metabolic processes as well as physiological and pathological changes in organisms [13]. Recent research has demonstrated that the interplay among urine pH, metabolite profiles, and the urinary microbiome provides new insights into urinary health and disease mechanisms [14]. Despite these advancements, the specific roles of the urinary microbiome and metabolome in CG, before and after treatment, are not yet fully understood. In the present study, high-throughput 16S rRNA sequencing and metabolomics were utilized to comprehensively investigate the urinary microbiome and metabolic profiles in normal women and those diagnosed with CG, both prior to and following treatment. This approach enabled a detailed evaluation of the microbial diversity present in the urinary tract, as well as the identification of metabolic signatures associated with CG states. The primary objective of this research was to assist clinicians and researchers in selecting research topics and identifying potential biomarkers for early diagnosis and personalized treatment plans. This was intended to contribute to the development of innovative diagnostic and therapeutic strategies, enhance patient outcomes, and improve the management of bladder disease. 2. Materials and Methods 2.1 Sample Collection. To conduct our study, we meticulously selected two groups of participants: five healthy women with an average age of 46.4 ± 3.23 years, designated as the C group, and six women with an average age of 45.7 ± 3.58 years who had been diagnosed with cystitis glandularis (CG) based on rigorous clinical and pathological criteria. Urine samples were collected from the healthy women (C group), as well as from the CG patients both before any treatment, forming the CG group, and after they had completed one week of a combined therapy regimen that included antibiotics and transurethral resection of lesions, constituting the CGP group. It is important to note that during the course of this study, two patients from the CGP group withdrew. To ensure the accuracy and scientific rigor of the research, all participants had maintained a habitual diet typical of the Central Plains of China—consisting mainly of wheat, corn, and vegetables—for at least one month prior to sample collection to minimize dietary variability. They had no known chronic or acute diseases, and had not used any antibiotics or other medications at least three months before participating in this study. To ensure the integrity and reliability of the samples, we adhered to stringent aseptic procedures throughout the collection process. Specifically, midstream urine was collected from the first morning urine sample of each participant to standardize the sample collection time. Immediately following collection, the urine samples were promptly placed into a portable liquid nitrogen tank to facilitate rapid freezing, minimizing potential degradation. Subsequently, the frozen urine samples were carefully transferred to a -80°C ultra-low temperature freezer for long-term storage, ensuring they remained in optimal condition for subsequent analysis. 2.2 High-throughput 16S rDNA gene sequencing. Frozen urine samples were carefully thawed to room temperature and subsequently subjected to centrifugation to separate the supernatant fluid, which was then retained. Following this step, total DNA was meticulously extracted from the urine samples using a specialized DNA extraction kit (manufactured by Magen, located in Guangzhou, China). The specific V3-V4 region of the 16S rDNA was targeted and amplified through polymerase chain reaction (PCR) using the primer sequences 341F (5'-CCTACGGGNGGCWGCAG-3') and 806R (5'-GGACTACHVGGGTATCTAAT-3'), which collectively span a region of approximately 466 base pairs. The resulting amplification products were verified by 2% agarose gel electrophoresis, a technique that allows visualization and confirmation of the amplified DNA fragments. To ensure accurate quantification, DNA concentration was measured using the Qubit 3.0 fluorometer, a device known for its precision in DNA quantification. The sequencing library, essential for subsequent high-throughput sequencing, was constructed using the Illumina DNA Prep Kit (Illumina, USA). Libraries that met stringent quality criteria were then subjected to sequencing on the Illumina Novaseq 6000 platform, employing paired-end 250 bp sequencing mode, which provides paired-end reads of 250 base pairs each. Post-sequencing, the raw reads underwent an initial filtering process to remove low-quality or contaminant sequences. Subsequently, paired-end reads were merged to form complete sequences. These merged sequences were further filtered to retain only high-quality clean tags. To eliminate chimeric sequences—artifacts that can skew the data—a clustering analysis was performed to identify and remove these chimeras. This process resulted in a set of high-quality clean tags free from contaminants and artifacts. Using these clean tags, operational taxonomic unit (OTU) abundance was calculated by employing the UPARSE algorithm, available in USEARCH (version 11.0.667), a software tool renowned for its efficiency in microbial community analysis. The OTU abundance data were then utilized for species annotation, leveraging the comprehensive SILVA database, version 132. This annotation provided insights into the species composition of the urine samples. Additionally, the data were subjected to alpha diversity analysis, which assesses diversity within a sample, and beta diversity analysis, which compares diversity between samples. Inter-group differential species comparison was also conducted to identify species that varied significantly between different groups. Finally, community function prediction was performed to infer the potential functional capabilities of the microbial communities present in the urine samples, using the method described in reference [ 15 ]. 2.3 Non-Targeted Metabolomics via LC-MS. Urine samples were carefully thawed at a controlled temperature of 4°C to ensure the integrity of the biological components. Once thawed, these samples were meticulously mixed with a precooled solution consisting of methanol, acetonitrile, and water in a precise volume ratio of 2:2:1 (v/v). This mixture was subsequently subjected to low-temperature ultrasonic treatment to enhance the extraction efficiency of the metabolites. Following this, the mixture underwent centrifugation to separate the supernatant from the particulate matter. The resulting supernatant was then carefully dried to remove any residual solvents, re-dissolved in an appropriate solvent, and centrifuged once again to ensure a clear and homogeneous solution; this second centrifugation aimed to remove any remaining particulates. Throughout the process, quality control (QC) samples were systematically injected at regular intervals to rigorously monitor the stability and performance of the analytical system. For the chromatographic and mass spectrometric analysis, an Agilent 1290 Infinity LC ultra-high performance liquid chromatography system was employed, following established protocols previously reported. The mass spectrometry data were acquired in both positive and negative ion modes to capture a comprehensive profile of the metabolites. A high-resolution LC-MS/MS platform was utilized to enhance sensitivity and accuracy. Metabolite identification was meticulously carried out by matching the exact mass—with a tolerance of less than 5 ppm—and the MS/MS fragmentation patterns against multiple reputable databases. These included the Human Metabolome Database (HMDB), LIPID MAPS, METLIN, and the Edinburgh Mouse Metabolic Database (EMDB) 2.0. The data preprocessing phase involved normalizing the samples using an internal standard to account for any variability in sample handling and instrument performance. Additionally, QC-based batch correction techniques were applied to ensure that the analytical results were consistent and reliable across all samples. Based on the identified metabolites, a series of analytical steps were performed sequentially. First, clustering analysis was conducted to group similar metabolites. Then, multivariate statistical analysis was applied to identify significant patterns and trends. Finally, differential metabolite identification and enrichment analyses were carried out to pinpoint specific metabolites altered under the experimental conditions, as detailed in reference [ 16 ]. 2.4 Correlation Analysis. We systematically computed the Pearson correlation coefficients and their respective P-values using the comprehensive data on the abundance levels of the dominant microbial populations and their corresponding metabolites. These Pearson correlation coefficient calculations were performed on the OmicShare platform (which is accessible at https://www.omicshare.com ). To visually represent the intricate relationships and degree of correlation between these microbial and metabolite variables, we generated a detailed and informative correlation heatmap. This graphical representation aids in clearly identifying statistically significant associations based on the calculated correlation coefficients and corresponding P-values within the dataset. 2.5 Statistical Testing. The data obtained from our experiments were expressed as mean ± standard deviation (mean ± SD). This statistical representation was derived from a rigorous experimental design involving three technical replicates for each individual test (thereby ensuring a sample size of n = 3 per group). To compare the differences between two distinct groups, we employed Welch's t-test, a statistical method well-suited for handling unequal variances. Additionally, for multi-group comparisons, we followed up with Tukey's post-hoc test. This approach allowed us to rigorously assess the significance of differences across multiple groups. Throughout these analyses, we adhered to a stringent criterion for statistical significance, setting the threshold at p < 0.05 to ensure the reliability and validity of our findings. 3. Results 3.1 Urine microbiota and the microbiome analysis To rigorously ensure the reliability and validity of the experimental outcomes, this study implemented specific and stringent quality control measures across all stages of experimental design, execution, and analytical procedures, including standardized sample preparation, sequencing quality filtering, and data normalization. During the sequencing phase, we carefully standardized and filtered the raw sequencing data amount for each individual sample, employing strict quality thresholds to guarantee that the final sequencing yield consistently surpassed 90% for every sample included in the analysis (as is clearly demonstrated in Fig. 1 A). For microbial community characterization, we systematically annotated microbiota using the obtained sequencing information, meticulously counting and analyzing the number of sequences across seven distinct taxonomic classification levels, ranging from domain to species, including phylum, class, order, family, and genus. Our detailed taxonomic analysis revealed that the genus level consistently contained the highest proportion of high-quality sequencing data (as is visually represented in Fig. 1 B). Based on this important finding, we strategically selected the genus level as the most appropriate taxonomic rank for conducting our subsequent in-depth analyses of microbial diversity and specificity. To visualize microbial distribution patterns, we generated a comprehensive heatmap at the genus level, where we specifically focused on taxa that demonstrated a relative abundance exceeding 0.1% based on Operational Taxonomic Unit (OTU) abundance calculations. This heatmap effectively illustrates both the abundance distribution patterns and the hierarchical clustering relationships among all analyzed samples (as shown in Fig. 1 C). Recognizing that dominant microbial populations play a pivotal role in shaping the ecological dynamics and functional architecture of microbial communities, we therefore identified and characterized the top 10 most abundant genera across all samples. These predominant genera were determined to be Vibrio, Enterococcus, Escherichia-Shigella, Gardnerella, Lactobacillus, Prevotella, Ralstonia, Bacteroides, Pediococcus, and Corynebacterium (as detailed in Fig. 1 D). Furthermore, to quantitatively assess microbial community differences between sample groups, we employed the widely used Adonis statistical test for multivariate analysis of variance. This analysis revealed that the observed inter-group variation was substantially greater (specifically 2.6 times greater) than the intra-group variation (F = 2.6132). Importantly, these between-group differences accounted for a significant 30.34% of the total observed variation in the dataset (R² = 0.3034, p = 0.005), as clearly depicted in Fig. 1 E. These statistically significant results provide compelling evidence for the existence of substantial and meaningful differences between the experimental groups under investigation. 3.2 Urine Microbiota Composition To comprehensively evaluate and compare the diversity of urine microbiota among three distinct groups—healthy women (C), cystitis glandularis (CG) patients before receiving any treatment, and CG patients after completing therapeutic interventions (CGP)—the microbial diversity sob index was meticulously calculated. The results demonstrated significant variations, with values of 570.6 for healthy controls, 1198.8 for untreated CG patients, and 1148.3 for treated CG patients (p = 0.0478). These quantitative findings clearly indicate that untreated CG patients exhibited the most pronounced microbiota diversity among all groups, as visually represented in Fig. 2 A. For a more detailed examination of the bacterial composition patterns across these groups, we conducted thorough analyses to identify both shared and unique microbial species. Our investigation revealed 112 bacterial species common to all three groups and identified group-specific distributions: 58 species were exclusively present in healthy controls, 134 species uniquely found in untreated CG patients, and 33 species specific to treated CG patients (Figs. 2 B and 2 C). These distribution patterns provide valuable insights into the microbial landscape associated with different health states. To further characterize these bacterial communities, we performed extensive classification based on comprehensive gene information obtained from three major databases: IMG, KEGG, and PATRIC. This classification system organized the microbiota into seven principal categories: Gram-positive bacteria, Gram-negative bacteria, biofilm-forming bacteria, potentially pathogenic bacteria, bacteria containing mobile genetic elements, oxygen-utilizing bacteria (including aerobic and facultatively anaerobic types), anaerobic bacteria, and stress-tolerant bacteria (Fig. 2 D). When conducting pairwise quantitative comparisons of functional phenotypes between untreated and treated CG patients, we observed no statistically significant differences (P > 0.05, data not shown). Nevertheless, more detailed analyses revealed important distinctions: compared to healthy controls, untreated CG patients showed significantly higher proportions of bacteria containing mobile genetic elements and Gram-negative bacteria (P < 0.05), along with notably reduced levels of anaerobic bacteria and Gram-positive bacteria (P < 0.05) (Fig. 2 E). Similarly, treated CG patients demonstrated increased abundance of bacteria containing mobile genetic elements and facultatively anaerobic bacteria (P < 0.05), coupled with significantly decreased anaerobic bacterial populations (P < 0.01) relative to healthy controls (Fig. 2 F). 3.3 Urine indicative bacteria Through comprehensive pairwise quantitative analysis of microbial abundance among different study groups, we observed distinct patterns of bacterial distribution. The results demonstrated that two key bacterial genera, Gardnerella and Prevotella, had significantly higher relative abundance in the healthy control group compared to other groups (P < 0.05). This is clearly illustrated in Fig. 3 A. Conversely, three bacterial taxa—Burkholderia-Caballeronia-Paraburkholderia, Candidatus Solibacter, and Bryobacter—showed consistently elevated levels in both untreated and treated chronic gastritis (CG) groups (P < 0.05). These findings are presented in Figs. 3 A and 3 B. Furthermore, our analysis identified a notable reduction in Clostridioides abundance within the treated group compared to the untreated group (P < 0.05), as depicted in Fig. 3 C. To further characterize the microbial signatures associated with each group and build upon the abundance findings, we conducted an in-depth indicator species analysis using the indicator value (indval) metric, which combines measures of specificity and occupancy. This analysis yielded several important findings: Gardnerella, Prevotella, and Lactobacillus emerged as highly significant indicator species for the healthy control group, with high indval scores of 0.98, 0.87, and 0.70, respectively (all P < 0.05). In the context of disease states, Escherichia-Shigella was strongly associated with untreated CG cases (indval = 0.88, P < 0.05), while Staphylococcus showed a particularly strong association with treated CG cases (indval = 0.94, P < 0.05). These relationships are visually represented in Fig. 3 D, providing clear evidence of distinct microbial profiles across the different study groups. 3.4 Urine metabolites and the metabolome analysis Metabolomic analysis meticulously examines a comprehensive array of low-molecular-weight metabolites present in urine samples. It employs both qualitative and quantitative methodologies to identify metabolites that exhibit significant differences in concentration or presence. To enhance detection coverage and ensure a thorough analysis, both positive (POS) and negative (NEG) ionization modes were utilized; 1,870 metabolites were detected in POS mode and 1,386 in NEG mode, as illustrated in Fig. 4 A. To visually represent the variations among the samples, a heatmap of metabolite clustering was generated, providing a clear illustration of the differences present (Fig. 4 B). Moreover, further analysis of the sample distribution revealed that the samples tended to cluster into intra-group clusters, which displayed significant differences compared to inter-group clusters, as shown in Fig. 4 C. To gain a deeper understanding of these group differences, additional analysis identified the top 20 most significant differential metabolites, highlighted in Fig. 4 D. Each of these metabolites demonstrated statistically significant differences between groups, with a P-value of less than 0.05, as depicted in Fig. 4 E. Notably, Kinetin was found to be abundant in the untreated CG group, while Ephedrine, N-acetylhistidine, and Fenpropidin were enriched in the healthy control group. The remaining 16 metabolites were abundant in the treated CG group, indicating distinct metabolic profiles associated with the untreated CG, treated CG, and healthy control conditions. 3.5 Correlation analysis between microbiome and metabolome Seven distinct types of bacteria—Gardnerella, Lactobacillus, Prevotella, Ralstonia, Staphylococcus, Vibrio, and Escherichia-Shigella—were found to be significantly abundant and highly indicative of different sample groups. These bacteria demonstrated notable correlations with the top 20 differential metabolites identified across these groups. Specifically, Gardnerella, Lactobacillus, and Prevotella showed positive correlations with several metabolites, including Ephedrine, N-acetylhistidine, Fenpropidin, and Acetylendicarboxylate. At the same time, they exhibited negative correlations with 16 other metabolites, indicating a complex interaction between these bacteria and the metabolites. In contrast, Staphylococcus and Vibrio displayed negative correlations with metabolites such as Ephedrine, Fenpropidin, and Acetylendicarboxylate but also showed positive correlations with 15 other metabolites, suggesting multifaceted relationships. Escherichia-Shigella demonstrated positive correlations with metabolites like Kinetin and Ephedrine, while showing negative correlations with 18 other metabolites, highlighting distinctive metabolic interactions. Ralstonia exhibited negative correlations with metabolites including Kinetin, Ephedrine, N-acetylhistidine, and Fenpropidin, and, interestingly, it showed no correlations with 16 additional metabolites, as illustrated in Fig. 5 A. Further statistical correlation analysis revealed that Vibrio had significant positive correlations with 12 metabolites (R > 0.5, P < 0.05) and a notable negative correlation with Fenpropidin (R = -0.6260, P = 0.0126), emphasizing its selective metabolic interactions. Staphylococcus demonstrated significant positive correlations with 11 metabolites (R > 0.5, P 0.5, P < 0.05), indicating its potential influence on these metabolites. Lactobacillus showed a strong positive correlation with N-acetylhistidine (R = 0.7120, P = 0.0029), further underscoring its significance. Lastly, Gardnerella revealed positive correlations with both Acetylendicarboxylate and Fenpropidin (R > 0.5, P < 0.05), as depicted in Fig. 5 B, highlighting the intricate relationships between these bacteria and the metabolites they interact with. 4. Discussion Most CG patients experience recurrent urinary tract infections, chronic bladder outlet obstruction, or stones. These conditions collectively lead to chronic irritation and inflammation. As a result, antibiotic treatment and symptom-focused transurethral lesion resection—procedures aimed at alleviating symptoms—are typically considered the preferred treatment methods [ 1 – 4 ]. However, these treatments have limited effectiveness, with a recurrence rate as high as 60% [ 5 , 6 ]. This study comprehensively revealed significant alterations in the urinary microbiota of female patients diagnosed with cystitis glandularis, as determined through advanced high-throughput 16S rRNA sequencing and detailed metabolomics analysis. Notably, there were marked changes in the abundance of specific bacterial populations, such as Escherichia-Shigella and Lactobacillus, when compared to healthy control subjects. These findings align well with previous research, as indicated by references [ 17 , 18 ]. Furthermore, this investigation delves into the underlying reasons for the less-than-satisfactory outcomes associated with current treatment strategies, examining these issues from the dual perspectives of microorganisms and metabolites. It particularly emphasizes that the disadvantages of antibiotic therapy for cystitis glandularis may, in fact, overshadow its potential benefits. Through a meticulous analysis of the microbiota and metabolites present in urine samples collected from both healthy controls and cystitis glandularis patients, both before and after treatment, it was discovered that beneficial bacteria such as Gardnerella, Lactobacillus, and Prevotella were abundant in the healthy control group but exhibited a significant reduction in the cystitis glandularis patients. Conversely, pathogenic bacteria including Acinetobacter, Bacteroides, Enterococcus, Vibrio, and Escherichia-Shigella were found to be more prevalent in the cystitis glandularis patients, while their presence was notably diminished in the healthy controls. This shift in microbial composition may play a crucial role in the pathogenesis of cystitis glandularis. Following treatment interventions, particularly the administration of antibiotics, there was a significant decrease in the abundance of Escherichia-Shigella, a slight reduction in Acinetobacter and Bacteroides, yet no significant impact on the levels of Enterococcus and Vibrio was observed. Additionally, Ralstonia and Staphylococcus, which were relatively uncommon in healthy controls and untreated conditions, showed a significant increase as a direct result of antibiotic treatment. Meanwhile, the meticulous detection of metabolites revealed that, among the top 20 differential metabolites identified across the various groups, a remarkable 15 metabolites exhibited a significantly increased abundance in the treatment group, indicating a notable biochemical response to the intervention. Further correlation analysis conducted between the indicator microorganisms and these top 20 differential metabolites unveiled a fascinating pattern: the bacteria that are indicative of the healthy control group, which included well-known genera such as Gardnerella, Lactobacillus, and Prevotella, demonstrated a positive correlation with several key metabolites, specifically ephedrine, N-acetylhistidine, fenpropidin, and acetylendicarboxylate. Conversely, these beneficial bacteria were found to be negatively correlated with 16 other metabolites, suggesting a complex interplay that contributes to the maintenance of microbial stability within the urinary environment. In stark contrast, the indicator bacteria Staphylococcus and Vibrio, which were prevalent in the treatment group, displayed a predominantly negative correlation with these four beneficial metabolites while showing a positive correlation with 15 other metabolites, indicating a shift in the microbial landscape. Previous studies have consistently shown a close association between alterations in metabolite profiles and the composition of microbial communities, as referenced in studies [19,20]. Therefore, the correlation analysis conducted in our study not only supports these previous findings but also infers that these intricate correlations may significantly influence the symptom presentation and disease progression associated with cystitis glandularis, highlighting the importance of understanding these relationships in the context of microbial health and disease. Therefore, the results presented above clearly indicate that the treatments with a primary focus on antibiotics, offer certain therapeutic benefits by significantly reducing the population of the Escherichia-Shigella group of bacteria. However, it is important to note that these treatments also disrupt the delicate balance of the normal microbial composition within the urinary tract, causing an imbalance of microorganisms and their associated metabolites, which ultimately results in suboptimal treatment outcomes that are less than ideal. This study, therefore, aims to clarify the underlying reasons behind these less-than-satisfactory outcomes by meticulously examining the changes that occur in both the microbiota and the metabolites before and after treatment. Specifically, it has been observed that current antibiotic treatments do not effectively or rapidly restore the microbial composition to a healthy state; rather, they may inadvertently exacerbate the existing microbial imbalance, leading to detrimental changes in the composition of metabolites that can further complicate the patient's recovery process. The high-throughput 16S rRNA sequencing and metabolite detection techniques employed in this study have proven to be remarkably effective in identifying a diverse array of microorganisms and metabolites present in the urine samples of both healthy controls and patients suffering from cystitis glandularis. This significant finding underscores the capabilities of these advanced techniques in the realms of microbiome and metabolome research, highlighting their potential to unveil intricate relationships within microbial communities. Furthermore, the application of this cutting-edge technology serves as a powerful tool that not only enhances our understanding of complex microbial ecology but also lays the groundwork for the development of innovative diagnostic and therapeutic strategies tailored specifically for cystitis glandularis. In any case, we must consider the limitations of the research. First, the small sample size may compromise the statistical power, reproducibility, and generalizability of the findings. Second, there is a lack of confirmatory experiments to support the biomarker identification, which introduces a degree of uncertainty regarding the validity of the results. Additionally, no functional validation has been provided to establish causality or biological relevance between microbes and metabolites. Given these limitations, collaborative efforts between researchers and clinicians are essential to advance this field. These efforts should focus on several key aspects. First, increasing the sample size will enhance generalizability, and incorporating longitudinal studies will help explore the temporal dynamics of the urinary microbiota and metabolites. Moreover, experimental validation through in vitro or in vivo studies is crucial. Such studies will clarify the causative roles of specific microbial taxa and metabolites in cystitis glandularis. We believe these comprehensive approaches will lead to more accurate diagnostic methods, more effective treatments, and ultimately improved patient outcomes in the future. In summary, our research has preliminarily uncovered some previously unreported phenomena by collecting clinical samples and applying sequencing-based microbial detection and bioinformatics methods. This study represents original research in clinical bioinformatics by providing novel insights into urinary microbiota and metabolite profiles. Although the sample size is relatively small, the use of stringent inclusion criteria, rigorous quality control, and accurate statistical methods helped ensure the consistency of the samples to the greatest extent possible. This approach, by minimizing the influence of extreme variations caused by other factors, also helped avoid the generation of outliers. Additionally, this study provides initial data supporting the characteristic features of urinary microbiota and metabolites in patients with cystitis glandularis. It also explains the reasons for the unsatisfactory current treatment methods from the perspectives of microbiota and metabolites. Specifically, this finding suggests that changes in urinary microbial composition may be an important factor in the pathogenesis of cystitis glandularis, but current treatments do not promptly restore the microbial composition to a healthy state. The findings not only contribute to the understanding of cystitis glandularis but also help clinicians identify relevant research questions and guide subsequent research directions toward innovative diagnostic and therapeutic approaches for early diagnosis and personalized treatment strategies. To build on these findings, future research should address the limitations identified in this study, particularly by expanding the sample size, incorporating longitudinal data, and conducting in vitro or in vivo studies. Such efforts would promote further clarification of the complex interplay between microbiota, metabolites, and cystitis glandularis, resulting in more accurate diagnostic methods and effective treatments to improve patient outcomes in the near future. Declarations Data availability All data generated or analyzed during this study are included in this article. The genome assembly of S. novakii NCAIM Y.00986 was deposited to GenBank, accession number JBJGDZ000000000 (https://www.ncbi.nlm.nih.gov/nuccore/JBJGDZ000000000), annotation was deposited in FigShare (https://doi.org/10.6084/m9.figshare.27640185). The raw data were deposited under NCBI BioProject PRJNA1183396 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1183396). Acknowledgments Thanks for the sequencing platform and bioinformation analysis of Gene Denovo Biotechnology Co., Ltd (Guangzhou, China). Funding This work was supported by grants from the National Natural Sciences Foundation of China (No.81500588). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Contributions Y. C: Funding acquisition, Experiments design, Experiments conduction and supervision, Conceptualization, Data analysis, Figures preparation, Writing– original draft, Writing– review & editing. T. L: Samples collection, Experiments conduction, Data analysis, Writing– original draft, Writing– review & editing. P. X: Samples collection, Experiments conduction, Data analysis, Writing– review & editing. J. F: Data analysis, Figures preparation, Writing– review & editing. X. Z: Experiments design, Resources, Writing– review & editing. K.K.E: Conceptualization, Supervision, Writing– review & editing. K.N: Conceptualization, Writing– review & editing. Ethics declarations All participants gave consent to supply the urine. Experiment Ethics was approved by the First Affiliated Hospital of Zhengzhou University (2023-KY-0627-001). Competing interests The authors declared no competing interests. Clinical trial number Not applicable. References Nasrallah OG, Balaghi A, El Sayegh N, Mahdi JH, Sinno S, Nasr RW. Florid Cystitis Glandularis with Intestinal Metaplasia in the Prostatic Urethra: a case report and review of the literature. Int J Surg Case Rep. 116:109416. doi:10.1016/j.ijscr.2024.109416. https://pubmed.ncbi.nlm.nih.gov/38422750/ Droupy S. The therapeutic approach to different forms of cystitis: impact on public health. Urologia. 2017;84(Suppl 1):8-15. doi:10.5301/uj.5000262. https://pubmed.ncbi.nlm.nih.gov/28862726/ Zhao C, Wang K, Men C, Xin Y, Xia H. The Efficacy and Safety of Transurethral 2 μm Laser Bladder Lesion Mucosal En Bloc Resection in the Treatment of Cystitis Glandularis. Front Med (Lausanne). 9:840378. Published 2022 None. doi:10.3389/fmed.2022.840378 Amunategui JPR, Echeverria D, Duchene A. Cystitis glandularis due to Escherichia coli infection in a diabetic Miniature Schnauzer: a case report. BMC Vet Res. 2025 Jan 17;21(1):25. doi: 10.1186/s12917-025-04472-x. Guan Y, Tan C, Xiao F, Li H, Liu H, Hu W, Song H, Zhu H. Effectiveness of transurethral resection combined with sapylin for bladder perfusion in glandular cystitis: A randomized controlled trial. Afr J Reprod Health. 2025 ;29(5s):27-34. doi: 10.29063/ajrh2025/v29i5s.4. Bai SJ, Chen XB, Zeng TB. Treatment Asian J Surg. 2023;46(6):2444. Stamatakos PV, Fragkoulis C, Zoidakis I, et al. A review of urinary bladder microbiome in patients with bladder cancer and its implications in bladder pathogenesis. World J Urol. 2024;42(1):457. Published 2024 Jul 29. doi:10.1007/s00345-024-05173-0 https://pubmed.ncbi.nlm.nih.gov/39073494/ Bukhari Y, Chow R, Xiang AJ, Lemos N. Long-Term Antibiotics for Disturbed Bladder Microbiome Disorders. Int Urogynecol J. . Published online May 6,2025. doi:10.1007/s00192-025-06145-7. https://pubmed.ncbi.nlm.nih.gov/40327075/ Zheng Z, Hu J, Li W, Ma K, Zhang C, Li K, Yao Y. Integrated microbiome and metabolome analysis reveals novel urinary microenvironmental signatures in interstitial cystitis/bladder pain syndrome patients. J Transl Med. 2023 Apr 19;21(1):266. doi: 10.1186/s12967-023-04115-5. Bae S, Chung H. The Urobiome and Its Role in Overactive Bladder. Int Neurourol J. 2022 Sep;26(3):190-200. doi: 10.5213/inj.2244016.008. Zhang J, Lei Y, Du H, Li Z, Wang X, Yang D, Gao F, Li J. Exploring urinary microbiome: insights into neurogenic bladder and improving management of urinary tract infections. Front Cell Infect Microbiol. 2025 Apr 1;15:1512891. doi: 10.3389/fcimb.2025.1512891 Rinschen MM, Ivanisevic J, Giera M, Siuzdak G. Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol. 2019 Jun;20(6):353-367. doi: 10.1038/s41580-019-0108-4. Papadimitropoulos MP, Vasilopoulou CG, Maga-Nteve C, Klapa MI. Untargeted GC-MS Metabolomics. Methods Mol Biol. 2018;1738:133-147. doi: 10.1007/978-1-4939-7643-0_9. Aliwa B, Horvath A, Traub J, Feldbacher N, Habisch H, Fauler G, Madl T, Stadlbauer V. Altered gut microbiome, bile acid composition and metabolome in sarcopenia in liver cirrhosis. J Cachexia Sarcopenia Muscle. 2023 Dec;14(6):2676-2691. doi: 10.1002/jcsm.13342. Yue N, Zhao H, Hu P, Zhang Y, Tian C, Kong C, et al. Real-world of Limosilactobacillus reuteri in mitigation of acute experimental colitis. J Nanobiotechnology. 2025;23(1): 65. doi: 10.1186/s12951-025-03158-8. Liu J, Jing W, Wang T, Hu Z, Lu H. Functional metabolomics revealed the dual-activation of cAMP-AMP axis is a novel therapeutic target of pancreatic cancer. Pharmacol Res. 2023, 187:106554. doi: 10.1016/j.phrs.2022.106554. Amunategui JPR, Echeverria D, Duchene A. Cystitis glandularis due to Escherichia coli infection in a diabetic Miniature Schnauzer: a case report. BMC Vet Res. 2025;21(1):25. Published 2025 Jan 17. doi:10.1186/s12917-025-04472-x https://pubmed.ncbi.nlm.nih.gov/39825317/ Sadahira T, Wada K, Araki M, et al. Efficacy of Lactobacillus vaginal suppositories for the prevention of recurrent cystitis: A phase II clinical trial. Int J Urol. 2021;28(10): 1026- 1031. doi:10.1111/iju.14636 https://pubmed.ncbi.nlm.nih.gov/34258813/ Ji J, Zhang S, Yuan M, et al. Fermented Rosa Roxburghii Tratt Juice Alleviates High-Fat Diet-Induced Hyperlipidemia in Rats by Modulating Gut Microbiota and Metabolites. Front Pharmacol. 13:883629. Published 2022 None. doi:10.3389/fphar.2022.883629 https://pubmed.ncbi.nlm.nih.gov/35668952/ Li Z, Jin Y, Zhao H, et al. Aurantio-Obtusin Regulates Gut Microbiota and Serum Metabolism to Alleviate High-Fat Diet-Induced Obesity-Associated Non-Alcoholic Fatty Liver Disease in Mice. Phytother Res. 2025;39(5):1946-1965. doi:10.1002/ptr.8459 https://pubmed.ncbi.nlm.nih.gov/39953693/. 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. We do this by developing innovative software and high quality services for the global research community. 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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-7543157","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":510714819,"identity":"ef63161b-1974-4be5-90b6-7344d9a1d179","order_by":0,"name":"Yan Chen#","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACCTiDh4Hhg4GNHWlaGGcUpCWTpoWZ58MhxgZCOvhnNx97zFNzB8joPSZtY3CAmYH98NENeC25cyzdmOfYMyDjXJp0jsEdPgaetLQb+LQYSOSYSfOwHWZguAFk5Bg8Ywa60IyAlvxv0jz/DjPIg7RYGBxmbCCsJYdNmrftMIMBSAsDMVokbqSZSc7tO8xjeOdcsmWPQVoyGyG/8M9Ifibx5tthObnbvQdv/PhjY8fPfvgYXi0gwASMdyBiYAHHERsh5SDA+ANCM38gRvUoGAWjYBSMPAAAHk9G0wr/6j0AAAAASUVORK5CYII=","orcid":"","institution":"the First Affiliated Hospital of Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Chen#","suffix":""},{"id":510714820,"identity":"21eaa692-4d8e-452a-bced-e0944aaeac55","order_by":1,"name":"Teng Li","email":"","orcid":"","institution":"the First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Teng","middleName":"","lastName":"Li","suffix":""},{"id":510714821,"identity":"8a70d763-2997-4db5-a149-51c62662d9d8","order_by":2,"name":"Pengchao Xu","email":"","orcid":"","institution":"the First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Pengchao","middleName":"","lastName":"Xu","suffix":""},{"id":510714822,"identity":"10325982-4a55-45cd-b6dc-c280c16db547","order_by":3,"name":"Jinjin Feng","email":"","orcid":"","institution":"the First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jinjin","middleName":"","lastName":"Feng","suffix":""},{"id":510714823,"identity":"e53e3a9a-f56b-4c10-a5c8-205504795085","order_by":4,"name":"Xuepei Zhang","email":"","orcid":"","institution":"the First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xuepei","middleName":"","lastName":"Zhang","suffix":""},{"id":510714824,"identity":"deab028a-ee06-4ce2-8aac-07246b26a355","order_by":5,"name":"Katariina Nurmi","email":"","orcid":"","institution":"University of Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Katariina","middleName":"","lastName":"Nurmi","suffix":""},{"id":510714825,"identity":"be02ec52-d318-4cd1-bd5d-bcbd10d9f527","order_by":6,"name":"Kari K. Eklund","email":"","orcid":"","institution":"University of Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Kari","middleName":"K.","lastName":"Eklund","suffix":""}],"badges":[],"createdAt":"2025-09-05 10:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7543157/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7543157/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90895932,"identity":"2c7402ee-c672-4a02-acf0-7813eb0dd9a5","added_by":"auto","created_at":"2025-09-09 11:36:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":170835,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Distribution of sequencing depth and effective read percentages for each sample. (B) Bar chart showing the percentage composition of each sample across seven taxonomic levels: domain, phylum, class, order, family, genus, and species. The genus level has a significantly higher percentage than the other levels. The genus level has a significantly higher percentage than the other levels. \u0026nbsp;(D) Stacked bar chart of genus-level distribution for each sample, showing the top 10 bacteria by mean abundance; remaining bacteria are grouped as Other, and unclassified bacteria are labeled as Unclassified. (E) Inter-group differences were analyzed using the Adonis test. F value represents inter-group variation. F=2.6132 indicates 2.6 times greater than intra-group variation. Effect size assessment: R² \u0026gt; 0.2 represents a strong explanatory effect; 0.1–0.2 represents a moderate effect; \u0026lt; 0.1 represents a weak effect. R² = 0.3034 indicates that the grouping variable explains 30.34% of the variation in community structure, showing a strong explanatory effect. C (healthy control), CG (untreated cystitis glandularis patients), CGP (patients treated with antibiotics and transurethral lesion resection). Statistical significance was determined by Tukey's post-hoc test; P \u0026lt; 0.05 and P \u0026lt; 0.01 indicate significant and highly significant differences, respectively.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7543157/v1/4e3234ca35faa109f29ede45.png"},{"id":90895935,"identity":"7e28dae8-0e77-4eb8-9da9-c0adae8e681d","added_by":"auto","created_at":"2025-09-09 11:36:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140365,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Results of microbiota diversity indices (sob index) at the genus level for each group. (B) Venn diagram showing genus-level bacteria among three groups. (C) Bar chart showing shared and unique genus-level bacteria among three groups. (D) Stacked chart of bacterial functional phenotypes. Based on gene information from the IMG, KEGG, and PATRIC databases, the differing bacterial communities among three groups were classified into Gram-positive bacteria, Gram-negative bacteria, biofilm-forming bacteria, potentially pathogenic bacteria, mobile element-containing bacteria, oxygen-utilizing bacteria (including aerobic, anaerobic, and facultatively anaerobic), and stress-tolerant bacteria. (E) Chart of quantitative analysis of functional phenotypes between healthy control and untreated CG patients by Welch's t-test. (F) Chart of quantitative analysis of functional phenotypes between healthy control and treated CG patients by Welch's t-test. C (healthy control), CG (untreated cystitis glandularis patients), CGP (patients treated with antibiotics and transurethral lesion resection). Statistical significance was determined by Welch's t-test or Tukey's post-hoc test; P \u0026lt; 0.05 and P \u0026lt; 0.01 indicate significant and highly significant differences, respectively.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7543157/v1/4923a5fa1cb28af0f0e414be.png"},{"id":90895930,"identity":"d356ce4a-d0c8-4733-a0ca-e554569e95bf","added_by":"auto","created_at":"2025-09-09 11:36:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":164864,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The chart of statistical differential bacteria among the top 10 abundance at the genus level between healthy control and untreated CG by Welch's t-test. (B) The chart of statistical differential bacteria among the top 10 abundance at the genus level between healthy control and treated CG by Welch's t-test. (C) The chart of statistical differential bacteria among the top 10 abundance at the genus level between untreated and treated CG by Welch's t-test. \u0026nbsp;(D) Bubble chart compares the top 10 indicative bacteria at the genus level among three groups by calculating indicator values, where 0 indicates no indication and 1 indicates complete exclusivity. Bubble size represents specificity to the target group, with values above 0.5 indicating significant specificity. The indicator value (IndVal), which is calculated as the product of Specificity and Occupancy (IndVal=Specificity×Occupancy); Specificity = Nindividuals\u003csub\u003eS, H\u003c/sub\u003e / Nindividuals\u003csub\u003eS\u003c/sub\u003e; Occupancy = Nsite\u003csub\u003eS, H\u003c/sub\u003e / Nsite\u003csub\u003eH\u003c/sub\u003e. Here, Nindividuals\u003csub\u003eS, H\u003c/sub\u003e denotes the mean abundance of species S averaged across all samples in group H; Nindividuals\u003csub\u003eS\u003c/sub\u003e is the sum of the mean abundances of species S across all groups; Nsite\u003csub\u003eS, H\u003c/sub\u003e represents the number of samples in group H where species S is present; and Nsite\u003csub\u003eH\u003c/sub\u003e indicates the total number of samples in group H. C (healthy control), CG (untreated cystitis glandularis patients), CGP (patients treated with antibiotics and transurethral lesion resection). Statistical significance was determined by Welch's t-test or Tukey's post-hoc test; P \u0026lt; 0.05 and P \u0026lt; 0.01 indicate significant and highly significant differences, respectively.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7543157/v1/ca072dd03e2f70fffce3e9d5.png"},{"id":90897861,"identity":"a7e4ec90-e3b4-400a-b142-84b7eb0da09d","added_by":"auto","created_at":"2025-09-09 11:52:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":284510,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Table showing metabolites detected in positive and negative ion modes. (B) Heatmap of clustered differential metabolites for each sample. Red indicates upregulation, blue indicates downregulation, and white indicates no difference. (C) PLS-DA score plots for three groups, showing samples clustering within groups and significant differences among the three groups. (D) VIP (Variable Importance in Projection) chart showing the top 20 differential metabolites among three groups. The x-axis represents VIP values, and the y-axis lists the metabolites. The right color bar shows metabolite abundance value, with red for up-regulation and blue for down-regulation. Higher VIP values indicate greater inter-group differences; VIP \u0026gt; 1 marks significant differences. (E) Statistical analysis table of VIP (Variable Importance in Projection) value differences. It lists VIP values and P-values for the top 20 differential metabolites among the three groups. VIP \u0026gt; 1 indicates significant inter-group differences. C (healthy control), CG (untreated cystitis glandularis patients), CGP (patients treated with antibiotics and transurethral lesion resection). Statistical significance was determined by Tukey's post-hoc test; P \u0026lt; 0.05 and P \u0026lt; 0.01 indicate significant and highly significant differences, respectively.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7543157/v1/8dcc5a2cb42da51bafbf003e.png"},{"id":90897435,"identity":"677b3f6a-5fb2-410d-8c98-45298d4abe1d","added_by":"auto","created_at":"2025-09-09 11:44:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":366115,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Heatmap showing the correlation between seven indicative bacteria and the top 20 differential metabolites. Red indicates a positive correlation, blue indicates a negative correlation, and white indicates no correlation. \u0026nbsp;(B) Correlation table displaying statistical correlations where P \u0026lt; 0.05. Positive R values indicate positive correlations, while negative R values indicate negative correlations. Absolute R values from 0.8 to 1.0 denote very strong correlations, 0.6 to 0.8 strong, 0.4 to 0.6 moderate, 0.2 to 0.4 weak, and 0 to 0.2 very weak or no correlations. Significance levels: P \u0026lt; 0.05 indicates significant correlation; P \u0026lt; 0.01 indicates highly significant correlation.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7543157/v1/3d6273c7c5b84647498ce0fc.png"},{"id":92451536,"identity":"6906a05f-93d3-40e1-817e-a33ce0721d7b","added_by":"auto","created_at":"2025-09-29 23:46:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1766906,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7543157/v1/b1aa1008-2242-4e31-b5fa-3b872f19c872.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating Urine Microbiota and Metabolites in Female Patients with Cystitis Glandularis: A Comprehensive Analysis Using High-Throughput 16S rRNA Sequencing and Metabolomics","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCystitis glandularis (CG) is a condition that originates from recurrent urinary tract infections (UTIs) and bladder dysfunction. It profoundly impacts patients\u0026apos; quality of life and imposes a significant economic burden on healthcare systems. This economic burden is largely due to the high costs associated with managing the condition, as documented in various studies [1,2]. Current treatment protocols primarily focus on symptomatic management, including antibiotic therapy and transurethral resection of bladder lesions [3,4]. However, the effectiveness of these treatments is increasingly compromised by the rising prevalence of antibiotic resistance and the adverse side effects associated with these therapies [4,5,6]. Consequently, there is an urgent need for innovative management strategies and novel treatment approaches to address this challenging condition.\u003c/p\u003e\n\u003cp\u003eIn recent years, scientific investigations have highlighted the crucial role of the urinary microbiome in maintaining bladder health and its involvement in disease processes [7,8]. Understanding these findings provides important context for exploring urinary diseases. Specifically, notable differences exist in the composition of the urinary microbiome between healthy individuals and those with lower urinary tract diseases. For instance, patients suffering from interstitial cystitis exhibit reduced diversity in their urinary microbiome along with an increased relative abundance of Lactobacillus [9]. Similarly, individuals with an overactive bladder show decreased microbiome diversity and an elevated abundance of specific bacteria, such as Gardnerella [10]. In neurogenic bladder patients, a higher proportion of bacteria like Enterococcus and Proteus is present in their urine [11].\u003c/p\u003e\n\u003cp\u003eThe metabolome, comprising a collection of small molecules integral to organism metabolism, plays a vital role in maintaining normal growth and function [12]. Metabolomic analysis involves a comprehensive qualitative and quantitative examination of all metabolites present in biological samples. This analysis aims to identify metabolites that show significant differences and biological relevance, thereby revealing underlying metabolic processes as well as physiological and pathological changes in organisms [13]. Recent research has demonstrated that the interplay among urine pH, metabolite profiles, and the urinary microbiome provides new insights into urinary health and disease mechanisms [14].\u003c/p\u003e\n\u003cp\u003eDespite these advancements, the specific roles of the urinary microbiome and metabolome in CG, before and after treatment, are not yet fully understood. In the present study, high-throughput 16S rRNA sequencing and metabolomics were utilized to comprehensively investigate the urinary microbiome and metabolic profiles in normal women and those diagnosed with CG, both prior to and following treatment. This approach enabled a detailed evaluation of the microbial diversity present in the urinary tract, as well as the identification of metabolic signatures associated with CG states. The primary objective of this research was to assist clinicians and researchers in selecting research topics and identifying potential biomarkers for early diagnosis and personalized treatment plans. This was intended to contribute to the development of innovative diagnostic and therapeutic strategies, enhance patient outcomes, and improve the management of bladder disease.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Sample Collection.\u003c/h2\u003e\n \u003cp\u003eTo conduct our study, we meticulously selected two groups of participants: five healthy women with an average age of 46.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23 years, designated as the C group, and six women with an average age of 45.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58 years who had been diagnosed with cystitis glandularis (CG) based on rigorous clinical and pathological criteria. Urine samples were collected from the healthy women (C group), as well as from the CG patients both before any treatment, forming the CG group, and after they had completed one week of a combined therapy regimen that included antibiotics and transurethral resection of lesions, constituting the CGP group. It is important to note that during the course of this study, two patients from the CGP group withdrew.\u003c/p\u003e\n \u003cp\u003eTo ensure the accuracy and scientific rigor of the research, all participants had maintained a habitual diet typical of the Central Plains of China\u0026mdash;consisting mainly of wheat, corn, and vegetables\u0026mdash;for at least one month prior to sample collection to minimize dietary variability. They had no known chronic or acute diseases, and had not used any antibiotics or other medications at least three months before participating in this study.\u003c/p\u003e\n \u003cp\u003eTo ensure the integrity and reliability of the samples, we adhered to stringent aseptic procedures throughout the collection process. Specifically, midstream urine was collected from the first morning urine sample of each participant to standardize the sample collection time. Immediately following collection, the urine samples were promptly placed into a portable liquid nitrogen tank to facilitate rapid freezing, minimizing potential degradation. Subsequently, the frozen urine samples were carefully transferred to a -80\u0026deg;C ultra-low temperature freezer for long-term storage, ensuring they remained in optimal condition for subsequent analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 High-throughput 16S rDNA gene sequencing.\u003c/h2\u003e\n \u003cp\u003eFrozen urine samples were carefully thawed to room temperature and subsequently subjected to centrifugation to separate the supernatant fluid, which was then retained. Following this step, total DNA was meticulously extracted from the urine samples using a specialized DNA extraction kit (manufactured by Magen, located in Guangzhou, China). The specific V3-V4 region of the 16S rDNA was targeted and amplified through polymerase chain reaction (PCR) using the primer sequences 341F (5\u0026apos;-CCTACGGGNGGCWGCAG-3\u0026apos;) and 806R (5\u0026apos;-GGACTACHVGGGTATCTAAT-3\u0026apos;), which collectively span a region of approximately 466 base pairs. The resulting amplification products were verified by 2% agarose gel electrophoresis, a technique that allows visualization and confirmation of the amplified DNA fragments.\u003c/p\u003e\n \u003cp\u003eTo ensure accurate quantification, DNA concentration was measured using the Qubit 3.0 fluorometer, a device known for its precision in DNA quantification. The sequencing library, essential for subsequent high-throughput sequencing, was constructed using the Illumina DNA Prep Kit (Illumina, USA). Libraries that met stringent quality criteria were then subjected to sequencing on the Illumina Novaseq 6000 platform, employing paired-end 250 bp sequencing mode, which provides paired-end reads of 250 base pairs each.\u003c/p\u003e\n \u003cp\u003ePost-sequencing, the raw reads underwent an initial filtering process to remove low-quality or contaminant sequences. Subsequently, paired-end reads were merged to form complete sequences. These merged sequences were further filtered to retain only high-quality clean tags. To eliminate chimeric sequences\u0026mdash;artifacts that can skew the data\u0026mdash;a clustering analysis was performed to identify and remove these chimeras. This process resulted in a set of high-quality clean tags free from contaminants and artifacts.\u003c/p\u003e\n \u003cp\u003eUsing these clean tags, operational taxonomic unit (OTU) abundance was calculated by employing the UPARSE algorithm, available in USEARCH (version 11.0.667), a software tool renowned for its efficiency in microbial community analysis. The OTU abundance data were then utilized for species annotation, leveraging the comprehensive SILVA database, version 132. This annotation provided insights into the species composition of the urine samples. Additionally, the data were subjected to alpha diversity analysis, which assesses diversity within a sample, and beta diversity analysis, which compares diversity between samples. Inter-group differential species comparison was also conducted to identify species that varied significantly between different groups. Finally, community function prediction was performed to infer the potential functional capabilities of the microbial communities present in the urine samples, using the method described in reference [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Non-Targeted Metabolomics via LC-MS.\u003c/h2\u003e\n \u003cp\u003eUrine samples were carefully thawed at a controlled temperature of 4\u0026deg;C to ensure the integrity of the biological components. Once thawed, these samples were meticulously mixed with a precooled solution consisting of methanol, acetonitrile, and water in a precise volume ratio of 2:2:1 (v/v). This mixture was subsequently subjected to low-temperature ultrasonic treatment to enhance the extraction efficiency of the metabolites. Following this, the mixture underwent centrifugation to separate the supernatant from the particulate matter. The resulting supernatant was then carefully dried to remove any residual solvents, re-dissolved in an appropriate solvent, and centrifuged once again to ensure a clear and homogeneous solution; this second centrifugation aimed to remove any remaining particulates. Throughout the process, quality control (QC) samples were systematically injected at regular intervals to rigorously monitor the stability and performance of the analytical system.\u003c/p\u003e\n \u003cp\u003eFor the chromatographic and mass spectrometric analysis, an Agilent 1290 Infinity LC ultra-high performance liquid chromatography system was employed, following established protocols previously reported. The mass spectrometry data were acquired in both positive and negative ion modes to capture a comprehensive profile of the metabolites. A high-resolution LC-MS/MS platform was utilized to enhance sensitivity and accuracy. Metabolite identification was meticulously carried out by matching the exact mass\u0026mdash;with a tolerance of less than 5 ppm\u0026mdash;and the MS/MS fragmentation patterns against multiple reputable databases. These included the Human Metabolome Database (HMDB), LIPID MAPS, METLIN, and the Edinburgh Mouse Metabolic Database (EMDB) 2.0.\u003c/p\u003e\n \u003cp\u003eThe data preprocessing phase involved normalizing the samples using an internal standard to account for any variability in sample handling and instrument performance. Additionally, QC-based batch correction techniques were applied to ensure that the analytical results were consistent and reliable across all samples. Based on the identified metabolites, a series of analytical steps were performed sequentially. First, clustering analysis was conducted to group similar metabolites. Then, multivariate statistical analysis was applied to identify significant patterns and trends. Finally, differential metabolite identification and enrichment analyses were carried out to pinpoint specific metabolites altered under the experimental conditions, as detailed in reference [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Correlation Analysis.\u003c/h2\u003e\n \u003cp\u003eWe systematically computed the Pearson correlation coefficients and their respective P-values using the comprehensive data on the abundance levels of the dominant microbial populations and their corresponding metabolites. These Pearson correlation coefficient calculations were performed on the OmicShare platform (which is accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.omicshare.com\u003c/span\u003e\u003c/span\u003e). To visually represent the intricate relationships and degree of correlation between these microbial and metabolite variables, we generated a detailed and informative correlation heatmap. This graphical representation aids in clearly identifying statistically significant associations based on the calculated correlation coefficients and corresponding P-values within the dataset.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Statistical Testing.\u003c/h2\u003e\n \u003cp\u003eThe data obtained from our experiments were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). This statistical representation was derived from a rigorous experimental design involving three technical replicates for each individual test (thereby ensuring a sample size of n\u0026thinsp;=\u0026thinsp;3 per group). To compare the differences between two distinct groups, we employed Welch\u0026apos;s t-test, a statistical method well-suited for handling unequal variances. Additionally, for multi-group comparisons, we followed up with Tukey\u0026apos;s post-hoc test. This approach allowed us to rigorously assess the significance of differences across multiple groups. Throughout these analyses, we adhered to a stringent criterion for statistical significance, setting the threshold at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to ensure the reliability and validity of our findings.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Urine microbiota and the microbiome analysis\u003c/h2\u003e\n \u003cp\u003eTo rigorously ensure the reliability and validity of the experimental outcomes, this study implemented specific and stringent quality control measures across all stages of experimental design, execution, and analytical procedures, including standardized sample preparation, sequencing quality filtering, and data normalization. During the sequencing phase, we carefully standardized and filtered the raw sequencing data amount for each individual sample, employing strict quality thresholds to guarantee that the final sequencing yield consistently surpassed 90% for every sample included in the analysis (as is clearly demonstrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). For microbial community characterization, we systematically annotated microbiota using the obtained sequencing information, meticulously counting and analyzing the number of sequences across seven distinct taxonomic classification levels, ranging from domain to species, including phylum, class, order, family, and genus. Our detailed taxonomic analysis revealed that the genus level consistently contained the highest proportion of high-quality sequencing data (as is visually represented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). Based on this important finding, we strategically selected the genus level as the most appropriate taxonomic rank for conducting our subsequent in-depth analyses of microbial diversity and specificity.\u003c/p\u003e\n \u003cp\u003eTo visualize microbial distribution patterns, we generated a comprehensive heatmap at the genus level, where we specifically focused on taxa that demonstrated a relative abundance exceeding 0.1% based on Operational Taxonomic Unit (OTU) abundance calculations. This heatmap effectively illustrates both the abundance distribution patterns and the hierarchical clustering relationships among all analyzed samples (as shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). Recognizing that dominant microbial populations play a pivotal role in shaping the ecological dynamics and functional architecture of microbial communities, we therefore identified and characterized the top 10 most abundant genera across all samples. These predominant genera were determined to be Vibrio, Enterococcus, Escherichia-Shigella, Gardnerella, Lactobacillus, Prevotella, Ralstonia, Bacteroides, Pediococcus, and Corynebacterium (as detailed in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003eFurthermore, to quantitatively assess microbial community differences between sample groups, we employed the widely used Adonis statistical test for multivariate analysis of variance. This analysis revealed that the observed inter-group variation was substantially greater (specifically 2.6 times greater) than the intra-group variation (F\u0026thinsp;=\u0026thinsp;2.6132). Importantly, these between-group differences accounted for a significant 30.34% of the total observed variation in the dataset (R\u0026sup2; = 0.3034, p\u0026thinsp;=\u0026thinsp;0.005), as clearly depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE. These statistically significant results provide compelling evidence for the existence of substantial and meaningful differences between the experimental groups under investigation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Urine Microbiota Composition\u003c/h2\u003e\n \u003cp\u003eTo comprehensively evaluate and compare the diversity of urine microbiota among three distinct groups\u0026mdash;healthy women (C), cystitis glandularis (CG) patients before receiving any treatment, and CG patients after completing therapeutic interventions (CGP)\u0026mdash;the microbial diversity sob index was meticulously calculated. The results demonstrated significant variations, with values of 570.6 for healthy controls, 1198.8 for untreated CG patients, and 1148.3 for treated CG patients (p\u0026thinsp;=\u0026thinsp;0.0478). These quantitative findings clearly indicate that untreated CG patients exhibited the most pronounced microbiota diversity among all groups, as visually represented in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA.\u003c/p\u003e\n \u003cp\u003eFor a more detailed examination of the bacterial composition patterns across these groups, we conducted thorough analyses to identify both shared and unique microbial species. Our investigation revealed 112 bacterial species common to all three groups and identified group-specific distributions: 58 species were exclusively present in healthy controls, 134 species uniquely found in untreated CG patients, and 33 species specific to treated CG patients (Figs. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). These distribution patterns provide valuable insights into the microbial landscape associated with different health states.\u003c/p\u003e\n \u003cp\u003eTo further characterize these bacterial communities, we performed extensive classification based on comprehensive gene information obtained from three major databases: IMG, KEGG, and PATRIC. This classification system organized the microbiota into seven principal categories: Gram-positive bacteria, Gram-negative bacteria, biofilm-forming bacteria, potentially pathogenic bacteria, bacteria containing mobile genetic elements, oxygen-utilizing bacteria (including aerobic and facultatively anaerobic types), anaerobic bacteria, and stress-tolerant bacteria (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003eWhen conducting pairwise quantitative comparisons of functional phenotypes between untreated and treated CG patients, we observed no statistically significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, data not shown). Nevertheless, more detailed analyses revealed important distinctions: compared to healthy controls, untreated CG patients showed significantly higher proportions of bacteria containing mobile genetic elements and Gram-negative bacteria (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), along with notably reduced levels of anaerobic bacteria and Gram-positive bacteria (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). Similarly, treated CG patients demonstrated increased abundance of bacteria containing mobile genetic elements and facultatively anaerobic bacteria (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), coupled with significantly decreased anaerobic bacterial populations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) relative to healthy controls (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Urine indicative bacteria\u003c/h2\u003e\n \u003cp\u003eThrough comprehensive pairwise quantitative analysis of microbial abundance among different study groups, we observed distinct patterns of bacterial distribution. The results demonstrated that two key bacterial genera, Gardnerella and Prevotella, had significantly higher relative abundance in the healthy control group compared to other groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This is clearly illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA. Conversely, three bacterial taxa\u0026mdash;Burkholderia-Caballeronia-Paraburkholderia, Candidatus Solibacter, and Bryobacter\u0026mdash;showed consistently elevated levels in both untreated and treated chronic gastritis (CG) groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings are presented in Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB. Furthermore, our analysis identified a notable reduction in Clostridioides abundance within the treated group compared to the untreated group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC.\u003c/p\u003e\n \u003cp\u003eTo further characterize the microbial signatures associated with each group and build upon the abundance findings, we conducted an in-depth indicator species analysis using the indicator value (indval) metric, which combines measures of specificity and occupancy. This analysis yielded several important findings: Gardnerella, Prevotella, and Lactobacillus emerged as highly significant indicator species for the healthy control group, with high indval scores of 0.98, 0.87, and 0.70, respectively (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the context of disease states, Escherichia-Shigella was strongly associated with untreated CG cases (indval\u0026thinsp;=\u0026thinsp;0.88, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while Staphylococcus showed a particularly strong association with treated CG cases (indval\u0026thinsp;=\u0026thinsp;0.94, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These relationships are visually represented in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD, providing clear evidence of distinct microbial profiles across the different study groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cem\u003e3.4 Urine metabolites and the metabolome analysis\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eMetabolomic analysis meticulously examines a comprehensive array of low-molecular-weight metabolites present in urine samples. It employs both qualitative and quantitative methodologies to identify metabolites that exhibit significant differences in concentration or presence. To enhance detection coverage and ensure a thorough analysis, both positive (POS) and negative (NEG) ionization modes were utilized; 1,870 metabolites were detected in POS mode and 1,386 in NEG mode, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA.\u003c/p\u003e\n \u003cp\u003eTo visually represent the variations among the samples, a heatmap of metabolite clustering was generated, providing a clear illustration of the differences present (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). Moreover, further analysis of the sample distribution revealed that the samples tended to cluster into intra-group clusters, which displayed significant differences compared to inter-group clusters, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC. To gain a deeper understanding of these group differences, additional analysis identified the top 20 most significant differential metabolites, highlighted in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD. Each of these metabolites demonstrated statistically significant differences between groups, with a P-value of less than 0.05, as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE. Notably, Kinetin was found to be abundant in the untreated CG group, while Ephedrine, N-acetylhistidine, and Fenpropidin were enriched in the healthy control group. The remaining 16 metabolites were abundant in the treated CG group, indicating distinct metabolic profiles associated with the untreated CG, treated CG, and healthy control conditions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Correlation analysis between microbiome and metabolome\u003c/h2\u003e\n \u003cp\u003eSeven distinct types of bacteria\u0026mdash;Gardnerella, Lactobacillus, Prevotella, Ralstonia, Staphylococcus, Vibrio, and Escherichia-Shigella\u0026mdash;were found to be significantly abundant and highly indicative of different sample groups. These bacteria demonstrated notable correlations with the top 20 differential metabolites identified across these groups. Specifically, Gardnerella, Lactobacillus, and Prevotella showed positive correlations with several metabolites, including Ephedrine, N-acetylhistidine, Fenpropidin, and Acetylendicarboxylate. At the same time, they exhibited negative correlations with 16 other metabolites, indicating a complex interaction between these bacteria and the metabolites. In contrast, Staphylococcus and Vibrio displayed negative correlations with metabolites such as Ephedrine, Fenpropidin, and Acetylendicarboxylate but also showed positive correlations with 15 other metabolites, suggesting multifaceted relationships. Escherichia-Shigella demonstrated positive correlations with metabolites like Kinetin and Ephedrine, while showing negative correlations with 18 other metabolites, highlighting distinctive metabolic interactions. Ralstonia exhibited negative correlations with metabolites including Kinetin, Ephedrine, N-acetylhistidine, and Fenpropidin, and, interestingly, it showed no correlations with 16 additional metabolites, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA.\u003c/p\u003e\n \u003cp\u003eFurther statistical correlation analysis revealed that Vibrio had significant positive correlations with 12 metabolites (R\u0026thinsp;\u0026gt;\u0026thinsp;0.5, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and a notable negative correlation with Fenpropidin (R = -0.6260, P\u0026thinsp;=\u0026thinsp;0.0126), emphasizing its selective metabolic interactions. Staphylococcus demonstrated significant positive correlations with 11 metabolites (R\u0026thinsp;\u0026gt;\u0026thinsp;0.5, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), reinforcing its role in the metabolic landscape. Prevotella exhibited positive correlations with Acetylendicarboxylate and N-acetylhistidine (R\u0026thinsp;\u0026gt;\u0026thinsp;0.5, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating its potential influence on these metabolites. Lactobacillus showed a strong positive correlation with N-acetylhistidine (R\u0026thinsp;=\u0026thinsp;0.7120, P\u0026thinsp;=\u0026thinsp;0.0029), further underscoring its significance. Lastly, Gardnerella revealed positive correlations with both Acetylendicarboxylate and Fenpropidin (R\u0026thinsp;\u0026gt;\u0026thinsp;0.5, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB, highlighting the intricate relationships between these bacteria and the metabolites they interact with.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMost CG patients experience recurrent urinary tract infections, chronic bladder outlet obstruction, or stones. These conditions collectively lead to chronic irritation and inflammation. As a result, antibiotic treatment and symptom-focused transurethral lesion resection\u0026mdash;procedures aimed at alleviating symptoms\u0026mdash;are typically considered the preferred treatment methods [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, these treatments have limited effectiveness, with a recurrence rate as high as 60% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study comprehensively revealed significant alterations in the urinary microbiota of female patients diagnosed with cystitis glandularis, as determined through advanced high-throughput 16S rRNA sequencing and detailed metabolomics analysis. Notably, there were marked changes in the abundance of specific bacterial populations, such as Escherichia-Shigella and Lactobacillus, when compared to healthy control subjects. These findings align well with previous research, as indicated by references [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, this investigation delves into the underlying reasons for the less-than-satisfactory outcomes associated with current treatment strategies, examining these issues from the dual perspectives of microorganisms and metabolites. It particularly emphasizes that the disadvantages of antibiotic therapy for cystitis glandularis may, in fact, overshadow its potential benefits. Through a meticulous analysis of the microbiota and metabolites present in urine samples collected from both healthy controls and cystitis glandularis patients, both before and after treatment, it was discovered that beneficial bacteria such as Gardnerella, Lactobacillus, and Prevotella were abundant in the healthy control group but exhibited a significant reduction in the cystitis glandularis patients. Conversely, pathogenic bacteria including Acinetobacter, Bacteroides, Enterococcus, Vibrio, and Escherichia-Shigella were found to be more prevalent in the cystitis glandularis patients, while their presence was notably diminished in the healthy controls. This shift in microbial composition may play a crucial role in the pathogenesis of cystitis glandularis. Following treatment interventions, particularly the administration of antibiotics, there was a significant decrease in the abundance of Escherichia-Shigella, a slight reduction in Acinetobacter and Bacteroides, yet no significant impact on the levels of Enterococcus and Vibrio was observed. Additionally, Ralstonia and Staphylococcus, which were relatively uncommon in healthy controls and untreated conditions, showed a significant increase as a direct result of antibiotic treatment.\u003c/p\u003e\u003cp\u003eMeanwhile, the meticulous detection of metabolites revealed that, among the top 20 differential metabolites identified across the various groups, a remarkable 15 metabolites exhibited a significantly increased abundance in the treatment group, indicating a notable biochemical response to the intervention. Further correlation analysis conducted between the indicator microorganisms and these top 20 differential metabolites unveiled a fascinating pattern: the bacteria that are indicative of the healthy control group, which included well-known genera such as Gardnerella, Lactobacillus, and Prevotella, demonstrated a positive correlation with several key metabolites, specifically ephedrine, N-acetylhistidine, fenpropidin, and acetylendicarboxylate. Conversely, these beneficial bacteria were found to be negatively correlated with 16 other metabolites, suggesting a complex interplay that contributes to the maintenance of microbial stability within the urinary environment. In stark contrast, the indicator bacteria Staphylococcus and Vibrio, which were prevalent in the treatment group, displayed a predominantly negative correlation with these four beneficial metabolites while showing a positive correlation with 15 other metabolites, indicating a shift in the microbial landscape. Previous studies have consistently shown a close association between alterations in metabolite profiles and the composition of microbial communities, as referenced in studies [19,20]. Therefore, the correlation analysis conducted in our study not only supports these previous findings but also infers that these intricate correlations may significantly influence the symptom presentation and disease progression associated with cystitis glandularis, highlighting the importance of understanding these relationships in the context of microbial health and disease.\u003c/p\u003e\u003cp\u003eTherefore, the results presented above clearly indicate that the treatments with a primary focus on antibiotics, offer certain therapeutic benefits by significantly reducing the population of the Escherichia-Shigella group of bacteria. However, it is important to note that these treatments also disrupt the delicate balance of the normal microbial composition within the urinary tract, causing an imbalance of microorganisms and their associated metabolites, which ultimately results in suboptimal treatment outcomes that are less than ideal. This study, therefore, aims to clarify the underlying reasons behind these less-than-satisfactory outcomes by meticulously examining the changes that occur in both the microbiota and the metabolites before and after treatment. Specifically, it has been observed that current antibiotic treatments do not effectively or rapidly restore the microbial composition to a healthy state; rather, they may inadvertently exacerbate the existing microbial imbalance, leading to detrimental changes in the composition of metabolites that can further complicate the patient's recovery process.\u003c/p\u003e\u003cp\u003eThe high-throughput 16S rRNA sequencing and metabolite detection techniques employed in this study have proven to be remarkably effective in identifying a diverse array of microorganisms and metabolites present in the urine samples of both healthy controls and patients suffering from cystitis glandularis. This significant finding underscores the capabilities of these advanced techniques in the realms of microbiome and metabolome research, highlighting their potential to unveil intricate relationships within microbial communities. Furthermore, the application of this cutting-edge technology serves as a powerful tool that not only enhances our understanding of complex microbial ecology but also lays the groundwork for the development of innovative diagnostic and therapeutic strategies tailored specifically for cystitis glandularis.\u003c/p\u003e\u003cp\u003eIn any case, we must consider the limitations of the research. First, the small sample size may compromise the statistical power, reproducibility, and generalizability of the findings. Second, there is a lack of confirmatory experiments to support the biomarker identification, which introduces a degree of uncertainty regarding the validity of the results. Additionally, no functional validation has been provided to establish causality or biological relevance between microbes and metabolites. Given these limitations, collaborative efforts between researchers and clinicians are essential to advance this field. These efforts should focus on several key aspects. First, increasing the sample size will enhance generalizability, and incorporating longitudinal studies will help explore the temporal dynamics of the urinary microbiota and metabolites. Moreover, experimental validation through \u003cem\u003ein vitro\u003c/em\u003e or \u003cem\u003ein vivo\u003c/em\u003e studies is crucial. Such studies will clarify the causative roles of specific microbial taxa and metabolites in cystitis glandularis. We believe these comprehensive approaches will lead to more accurate diagnostic methods, more effective treatments, and ultimately improved patient outcomes in the future.\u003c/p\u003e\u003cp\u003eIn summary, our research has preliminarily uncovered some previously unreported phenomena by collecting clinical samples and applying sequencing-based microbial detection and bioinformatics methods. This study represents original research in clinical bioinformatics by providing novel insights into urinary microbiota and metabolite profiles. Although the sample size is relatively small, the use of stringent inclusion criteria, rigorous quality control, and accurate statistical methods helped ensure the consistency of the samples to the greatest extent possible. This approach, by minimizing the influence of extreme variations caused by other factors, also helped avoid the generation of outliers. Additionally, this study provides initial data supporting the characteristic features of urinary microbiota and metabolites in patients with cystitis glandularis. It also explains the reasons for the unsatisfactory current treatment methods from the perspectives of microbiota and metabolites. Specifically, this finding suggests that changes in urinary microbial composition may be an important factor in the pathogenesis of cystitis glandularis, but current treatments do not promptly restore the microbial composition to a healthy state. The findings not only contribute to the understanding of cystitis glandularis but also help clinicians identify relevant research questions and guide subsequent research directions toward innovative diagnostic and therapeutic approaches for early diagnosis and personalized treatment strategies. To build on these findings, future research should address the limitations identified in this study, particularly by expanding the sample size, incorporating longitudinal data, and conducting in vitro or in vivo studies. Such efforts would promote further clarification of the complex interplay between microbiota, metabolites, and cystitis glandularis, resulting in more accurate diagnostic methods and effective treatments to improve patient outcomes in the near future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article. The genome assembly of S. novakii NCAIM Y.00986 was deposited to GenBank, accession number JBJGDZ000000000 (https://www.ncbi.nlm.nih.gov/nuccore/JBJGDZ000000000), annotation was deposited in FigShare (https://doi.org/10.6084/m9.figshare.27640185). The raw data were deposited under NCBI BioProject PRJNA1183396 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1183396).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks for the sequencing platform and bioinformation analysis of Gene Denovo Biotechnology Co., Ltd (Guangzhou, China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Sciences Foundation of China (No.81500588). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY. C: Funding acquisition, Experiments design, Experiments conduction and supervision, Conceptualization, Data analysis, Figures preparation, Writing\u0026ndash; original draft, Writing\u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT. L: Samples collection, Experiments conduction, Data analysis, Writing\u0026ndash; original draft, Writing\u0026ndash; review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eP. X: Samples collection, Experiments conduction, Data analysis, Writing\u0026ndash; review \u0026amp; editing. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJ. F: Data analysis, Figures preparation, Writing\u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eX. Z: Experiments design, Resources, Writing\u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eK.K.E: Conceptualization, Supervision, Writing\u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eK.N: Conceptualization, Writing\u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants gave consent to supply the urine. Experiment Ethics was approved by the First Affiliated Hospital of Zhengzhou University (2023-KY-0627-001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eNasrallah OG, Balaghi A, El Sayegh N, Mahdi JH, Sinno S, Nasr RW. Florid Cystitis Glandularis with Intestinal Metaplasia in the Prostatic Urethra: a case report and review of the literature. Int J Surg Case Rep. 116:109416. doi:10.1016/j.ijscr.2024.109416. https://pubmed.ncbi.nlm.nih.gov/38422750/\u003c/li\u003e\n \u003cli\u003eDroupy S. The therapeutic approach to different forms of cystitis: impact on public health. Urologia. 2017;84(Suppl 1):8-15. doi:10.5301/uj.5000262. https://pubmed.ncbi.nlm.nih.gov/28862726/\u003c/li\u003e\n \u003cli\u003eZhao C, Wang K, Men C, Xin Y, Xia H. The Efficacy and Safety of Transurethral 2 \u0026mu;m Laser Bladder Lesion Mucosal En Bloc Resection in the Treatment of Cystitis Glandularis. Front Med (Lausanne). 9:840378. Published 2022 None. doi:10.3389/fmed.2022.840378\u003c/li\u003e\n \u003cli\u003eAmunategui JPR, Echeverria D, Duchene A. Cystitis glandularis due to Escherichia coli infection in a diabetic Miniature Schnauzer: a case report. BMC Vet Res. 2025 Jan 17;21(1):25. doi: 10.1186/s12917-025-04472-x.\u003c/li\u003e\n \u003cli\u003eGuan Y, Tan C, Xiao F, Li H, Liu H, Hu W, Song H, Zhu H. Effectiveness of transurethral resection combined with sapylin for bladder perfusion in glandular cystitis: A randomized controlled trial. Afr J Reprod Health. 2025 ;29(5s):27-34. doi: 10.29063/ajrh2025/v29i5s.4.\u003c/li\u003e\n \u003cli\u003eBai SJ, Chen XB, Zeng TB. Treatment Asian J Surg. 2023;46(6):2444.\u003c/li\u003e\n \u003cli\u003eStamatakos PV, Fragkoulis C, Zoidakis I, et al. A review of urinary bladder microbiome in patients with bladder cancer and its implications in bladder pathogenesis. World J Urol. 2024;42(1):457. Published 2024 Jul 29. doi:10.1007/s00345-024-05173-0 https://pubmed.ncbi.nlm.nih.gov/39073494/\u003c/li\u003e\n \u003cli\u003eBukhari Y, Chow R, Xiang AJ, Lemos N. Long-Term Antibiotics for Disturbed Bladder Microbiome Disorders. Int Urogynecol J. . Published online May 6,2025. doi:10.1007/s00192-025-06145-7. https://pubmed.ncbi.nlm.nih.gov/40327075/\u003c/li\u003e\n \u003cli\u003eZheng Z, Hu J, Li W, Ma K, Zhang C, Li K, Yao Y. Integrated microbiome and metabolome analysis reveals novel urinary microenvironmental signatures in interstitial cystitis/bladder pain syndrome patients. J Transl Med. 2023 Apr 19;21(1):266. doi: 10.1186/s12967-023-04115-5.\u003c/li\u003e\n \u003cli\u003eBae S, Chung H. The Urobiome and Its Role in Overactive Bladder. Int Neurourol J. 2022 Sep;26(3):190-200. doi: 10.5213/inj.2244016.008.\u003c/li\u003e\n \u003cli\u003eZhang J, Lei Y, Du H, Li Z, Wang X, Yang D, Gao F, Li J. Exploring urinary microbiome: insights into neurogenic bladder and improving management of urinary tract infections. Front Cell Infect Microbiol. 2025 Apr 1;15:1512891. doi: 10.3389/fcimb.2025.1512891\u003c/li\u003e\n \u003cli\u003eRinschen MM, Ivanisevic J, Giera M, Siuzdak G. Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol. 2019 Jun;20(6):353-367. doi: 10.1038/s41580-019-0108-4.\u003c/li\u003e\n \u003cli\u003ePapadimitropoulos MP, Vasilopoulou CG, Maga-Nteve C, Klapa MI. Untargeted GC-MS Metabolomics. Methods Mol Biol. 2018;1738:133-147. doi: 10.1007/978-1-4939-7643-0_9.\u003c/li\u003e\n \u003cli\u003eAliwa B, Horvath A, Traub J, Feldbacher N, Habisch H, Fauler G, Madl T, Stadlbauer V. Altered gut microbiome, bile acid composition and metabolome in sarcopenia in liver cirrhosis. J Cachexia Sarcopenia Muscle. 2023 Dec;14(6):2676-2691. doi: 10.1002/jcsm.13342.\u003c/li\u003e\n \u003cli\u003eYue N, Zhao H, Hu P, Zhang Y, Tian C, Kong C, et al. Real-world of Limosilactobacillus reuteri in mitigation of acute experimental colitis. J Nanobiotechnology. 2025;23(1): 65. doi: 10.1186/s12951-025-03158-8.\u003c/li\u003e\n \u003cli\u003eLiu J, Jing W, Wang T, Hu Z, Lu H. Functional metabolomics revealed the dual-activation of cAMP-AMP axis is a novel therapeutic target of pancreatic cancer. Pharmacol Res. 2023, 187:106554. doi: 10.1016/j.phrs.2022.106554.\u003c/li\u003e\n \u003cli\u003eAmunategui JPR, Echeverria D, Duchene A. Cystitis glandularis due to Escherichia coli infection in a diabetic Miniature Schnauzer: a case report. BMC Vet Res. 2025;21(1):25. Published 2025 Jan 17. doi:10.1186/s12917-025-04472-x https://pubmed.ncbi.nlm.nih.gov/39825317/\u003c/li\u003e\n \u003cli\u003eSadahira T, Wada K, Araki M, et al. Efficacy of Lactobacillus vaginal suppositories for the prevention of recurrent cystitis: A phase II clinical trial. Int J Urol. 2021;28(10): 1026- 1031. doi:10.1111/iju.14636 https://pubmed.ncbi.nlm.nih.gov/34258813/\u003c/li\u003e\n \u003cli\u003eJi J, Zhang S, Yuan M, et al. Fermented \u0026lt;i\u0026gt;Rosa Roxburghii\u0026lt;/i\u0026gt; Tratt Juice Alleviates High-Fat Diet-Induced Hyperlipidemia in Rats by Modulating Gut Microbiota and Metabolites. Front Pharmacol. 13:883629. Published 2022 None. doi:10.3389/fphar.2022.883629 https://pubmed.ncbi.nlm.nih.gov/35668952/\u003c/li\u003e\n \u003cli\u003eLi Z, Jin Y, Zhao H, et al. Aurantio-Obtusin Regulates Gut Microbiota and Serum Metabolism to Alleviate High-Fat Diet-Induced Obesity-Associated Non-Alcoholic Fatty Liver Disease in Mice. Phytother Res. 2025;39(5):1946-1965. doi:10.1002/ptr.8459 https://pubmed.ncbi.nlm.nih.gov/39953693/.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Glandular Cystitis, Microbiota, Metabolites, Urine, Correlation Analysis of microbiota and metabolites","lastPublishedDoi":"10.21203/rs.3.rs-7543157/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7543157/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCystitis Glandularis (CG) is a condition that negatively impacts the quality of life in women, often resulting in recurrent urinary tract infections and bladder dysfunction. Existing antibiotic therapies and symptom-focused transurethral resection have proven inadequate. This study aimed to investigate the urinary microbiota diversity and metabolic complexity in female patients with CG before and after treatment, using high-throughput 16S rRNA sequencing and metabolomics analysis. The results indicated a significant reduction in beneficial genera such as Gardnerella, Lactobacillus, and Prevotella in CG patients compared to healthy controls, while an increase was observed in pathogenic taxa such as Acinetobacter, Bacteroides, Enterococcus, Vibrio, and Escherichia-Shigella. Moreover, following antibiotic treatment, a notable decrease in Escherichia-Shigella was observed, along with a slight reduction in Acinetobacter and Bacteroides; however, the abundance of Enterococcus and Vibrio remained unchanged. Additionally, antibiotic treatment correlated with an increase in Ralstonia and Staphylococcus. Metabolomic profiling revealed that 15 out of the top 20 differential metabolites were significantly increased in the treatment group compared to pre-treatment levels. Correlation analyses showed that bacteria associated with healthy controls were positively linked with metabolites such as ephedrine and N-acetylhistidine. In contrast, treatment-associated bacteria, Staphylococcus and Vibrio, exhibited opposite correlations. These findings suggest that current antibiotic treatments are insufficient in restoring microbial equilibrium, potentially exacerbating microbial dysbiosis and metabolic imbalances, thereby contributing to suboptimal outcomes in CG management. This highlights the need for alternative therapeutic strategies to maintain microbial health and enhance treatment efficacy.\u003c/p\u003e","manuscriptTitle":"Investigating Urine Microbiota and Metabolites in Female Patients with Cystitis Glandularis: A Comprehensive Analysis Using High-Throughput 16S rRNA Sequencing and Metabolomics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 11:36:23","doi":"10.21203/rs.3.rs-7543157/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":"56bf41ed-7ffc-4d44-9801-3b52e9a07955","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54252046,"name":"Health sciences/Diseases"},{"id":54252047,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2025-09-29T23:38:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 11:36:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7543157","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7543157","identity":"rs-7543157","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}