Correlations between microbiota-derived metabolites and cervical precancerous lesions in women with HPV

Correlations between microbiota-derived metabolites and cervical precancerous lesions in women with HPV | 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 Correlations between microbiota-derived metabolites and cervical precancerous lesions in women with HPV Su Shen, Shixian Zhao, Jinjun Shan, Qingling Ren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3066333/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 High-risk human papillomavirus (HR-HPV) causes cervical squamous intraepithelial lesions and cervical cancer; however, only a small group of people infected with HR-HPV will develop cervical precancerous lesions or cervical cancer. Current studies have shown that an imbalance in the cervicovaginal flora may be one of the factors for persistent HR-HPV infection. Cervicovaginal secretions are easily accessible and may be advantageous tools for diagnosing risks for cervical cancer. Thus, in this pilot study we collected 156 cervicovaginal secretions of women with HPV infection with precancerous cervical lesions to determine whether microflora-derived metabolites present in the secretion can be used for assessing the risk of cervical cancer in patients. We performed 16S rRNA sequencing and metabolomic analyses to identify changes in the cervicovaginal flora and its metabolites in patients with HPV infection with different grades of cervical lesions. We detected 164 common known metabolites in the three groups of samples. There are significant differences in the metabolic patterns of cervical lesion groups with different degrees, and multiple metabolites are significantly related to the vaginal flora. Among them, the content of metabolites such as taurine, guanine, and uric acid are significantly different in patients with persistent HR-HPV infection with different degrees of cervical lesions. Taurine is positively correlated with Weissella, and negatively correlated with Corynebacterium. Related. Which may involve 7 related metabolic pathways. Metabolites such as taurine, guanine, and uric acid are closely related to the activation of the human immune system. Cervical vaginal flora can regulate the synthesis and decomposition of amino acids, purines, and uric acid, and may play a major role in activating inflammation and the immune system. These metabolisms It is worthy of further research to assess the risk of HR-HPV infection progressing to cervical cancer. Biological sciences/Microbiology/Clinical microbiology Health sciences/Biomarkers/Predictive markers Health sciences/Diseases/Urogenital diseases HR-HPV SIL Cervicovaginal microecology 16S rRNA sequencing metabolite Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Cervical cancer is one of the most common gynecological malignancies, with a high incidence rate and a youthful trend [ 3 – 4 ]. Cervical cancer has become a major social problem that threatens women’s health and quality of life. Cervical squamous intraepithelial lesion (SIL) is a common disease that endangers women’s health. It is a group of cervical lesions that are closely related to invasive cervical cancer. High-risk human papillomavirus (HR-HPV) was found in nearly 90% of cervical SIL and 99% of cervical cancer tissues by a 15-year population-based cohort study from China[ 5 ]. Current studies have shown that an imbalance in the vaginal flora is associated with bacterial vaginosis and infectious diseases. The outcome of HPV infection has been associated with both host and viral factors, and with balance in the vaginal microenvironment [ 6 ]. Several studies have confirmed that an imbalance in the cervicovaginal flora may lead to persistent HR-HPV infection and may be involved in the development of SIL [ 7 – 8 ]. However, the mechanisms remain unclear and require further understanding. According to published reports, HPV infection affects mucosal metabolism, host immune status, and can cause changes in the vaginal microbiota [ 9 ], the converse is true as vaginal microbiota, host immunity can also have an impact on HPV infection [ 11 , 14 ]. HPV-infected individuals have a highly diverse cervicovaginal microbiome [ 32 ]. HPV infection and clearance rates are related to the increased Langerhans cells and the composition of the vaginal microbiome dominated by Lactobacillus geeseri [ 10 , 14 ]. The persistence of HR-HPV infection has been correlated with an imbalance in the vaginal microecology and with damage to the cervicovaginal mucosal barrier [ 12 ]. A myriad of bacteria in the cervicovaginal environment, with Lactobacillus species , thought to be biomarkers for cervicovaginal health, being frequently identified [ 13 ]. They coordinate with and restrict each other to form the vaginal micro-ecosystem. When the host's body changes, the vaginal microflora will also change in composition, breaking the original balance and causing a variety of inflammations and viral infections. In a 16-week cohort study, the composition of the vaginal flora of HPV-positive women was found to be dominated by Lactobacillus iners and anaerobic bacteria. Additionally, women with vaginal CST IV had an HPV positivity rate of 72% [ 14 ]. Another study has shown that Lactobacillus indolent may play a non-beneficial role in persistent HPV infection. Compare to women with Lactobacillus crispatus , HPV infection has been reported to have lower clearance rates and persisted longer in women with Lactobacillus inert [ 15 ]. The certain cervicovaginal bacteria, including Lactobacillus though with differing level of protection, have been associated with the natural history of HPV infection [ 15 ]. Some studies have reported that ciliates and Brucella in the vagina are closely linked to HR-HPV infection [ 16 – 17 ]. Studies have also found that Lactobacillus stimulates the phagocytic ability of host phagocytes and cells to produce various cytokines such as interleukin-10 (IL-10), IL-12, interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), etc. to enhance the immune capacity of the host [ 18 ]. A study has also shown that in the case of women lacking L. crispatus , the competitive proliferation of three bacterial groups, L. iners , Gardnerella vaginalis , and Anaerococcus vaginalis , leads to the development of SIL [ 19 ]. Some studies have shown that Lactobacillus has a plasmid-like function, which can integrate HPV16 gene, thereby preventing the virus from integrating with host cells [ 20 ]. The loss of the dominant position of Lactobacillus will promote the colonization of anaerobic bacterial species and increase the diversity of microorganisms. This usually causes changes in immunity and epithelial homeostasis through a variety of mechanisms, which in turn promotes HPV infection [ 22 ]. Chronic mucosal inflammation is also considered to be the core of HPV-induced cervical cancer [ 21 ]. Chronic inflammation of the cervix and vagina can cause cervical epithelial degeneration and necrosis, leading to the destruction of the cervical epithelial barrier, which triggers an immune response. This also makes the cervix and vagina susceptible to the reproduction of other pathogenic microorganisms, thereby contributing to the persistence of HPV and ultimately promoting the occurrence of SIL [ 23 ]. Recent studies have shown that different metabolites produced by different bacterial groups may have disease-related or protective roles. Metabolites of cervical microorganisms may enter systemic circulation and impact the overall health of high-risk cervical cancer patients [ 24 ]. Taken together, these studies show that an imbalance of the vaginal flora not only impairs immune responses but also promotes the persistence of HR-HPV infection and the progression of SIL. The changes in metabolites caused by the vaginal flora may reveal risks of cervical cancer patients with HR-HPV infection [ 15 ]. Cervical lavage fluid is derived from secretions of the cervicovaginal cavity. Collection of the cervical lavage fluid is easy and non-invasive as compared to the collection of cervix exfoliated cells. Thus, it may serve as an accessible tool for monitoring biomarkers to assess the risks of cervical cancer in women with HR-HPV infection. Therefore, in this study, we collected cervicovaginal lavage fluid samples from women with HPV infection with different degrees of cervical lesions. We identified the corresponding changes to the microflora and its metabolites to mine for potential biomarkers that can be used to predict the risk of cervical cancer. The findings we present here provide insights into diagnostic and monitoring approaches to help prevent the development of cervical cancer in women with HPV infection. Materials and methods 1. Subjects of the study From May to July 2020, a total of 156 women with HR-HPV were recruited from the Department of Gynecology, Jiangsu Hospital of Traditional Chinese Medicine. Patients whose thinprep cytologic test (TCT) showed atypical squamous cells of undetermined significance (ASCUS) and above lesions need colposcopy and cervical biopsy. All the patients knowingly and voluntarily cooperated with the examination. The patients were divided into groups based on the results of the cervical biopsy: no intraepithelial cell lesions (NILM group), low-grade cervical squamous intraepithelial lesions (LSIL group), and high-grade cervical squamous intraepithelial lesions (HSIL group). 2. Inclusion and exclusion criteria The inclusion criteria were: (1) ≥ 30 years of age, non-menopausal women; (2) sexual history, regular menstruation and the menstrual period was not reached when the sample was taken; (3) patients without BV, AV, STI (such as chlamydia) and not using hormonal contraception; (4) complete clinical data. The exclusion criteria were: (1) total hysterectomy; (2) history of immune-related diseases or use of immunosuppressive agents; (3) major diseases such as those of the heart, lung, and kidney; (4) medication for cervical disease within 1 month prior to the study; (5) pregnancy or breastfeeding. 3. Collection of cervicovaginal secretions and cervicovaginal lavage fluid Informed consent was obtained before sample collection, The patients did not use suppositories or flushing drugs within the first three days of sample collection and had no sexual activity within 48 hours before collection. A sterile cotton swab was used to collect the secretions at the cervix and a third of the vaginal side wall of patients in the lithotomy position. The swabs were then placed in a sterile test tube for routine leucorrhea detection within 15 min. The cervix and the upper a third of the vaginal wall were washed with 5 mL 0.9% sodium chloride solution, and 2 mL of the lavage fluid was withdrawn. The lavage fluid samples centrifuged at 4°C, 18,000 rpm for 10 min, and the supernatant and precipitate were stored at − 80°C. 4. Metabolomic analysis of cervicovaginal lavage samples The supernatant of the lavage samples were used for metabolomic analysis. The supernatant was made into lyophilized supernatant. Lyophilized supernatants of the lavage samples (500 µL) were reconstituted with 100µL sterile distilled water. Then, to each sample, 12.5 µg/mL ice-cold methanol solution containing myristic acid (400 µL) was added. The solutions were vortexed for 3 min and centrifuged for 10 mins (4℃, 18,000 rpm). Then, 200µL of the supernatant was transferred to a centrifugal concentrator to evaporate for 2 h. Thirty microliters of 10mg/mL methoxyamine pyridine was added. The samples were then vortexed for 5 mins and incubated at 30°C for 1.5 h with shaking (450 rpm). Next, 30 µL N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) was added to the samples; the samples were vortexed for 5 mins and incubated 37°C for 0.5 h with shaking (450 rpm). The samples were then centrifuged for 10 min (4°C, 18,000 rpm), and 50 µL of the supernatant was used for metabolite analysis by trace 1310 gas chromatography and TSQ 8000 mass spectrometer equipped with AS 1310 automatic injector; Tg-5ms Gas Chromatography Column (0.25 mm * 30 m, 0.25 um) (GC-MS, Thermo, Waltham, USA). At the same time, 3 µL supernatant was taken from all samples and the same amount was mixed to prepare quality control (QC) samples for methodological verification. After running a sequence of 10 samples, one QC sample was analyzed to evaluate the applicability of the system and the stability of the system. The obtained original file was preprocessed with Xcalibur 2.2 (Thermo, Waltham, USA) and normalized using the internal standard to obtain the metabolite name and corresponding peak value. 5. DNA extraction, 16S rRNA sequencing, and operational taxonomic unit (OTU) clustering According to the E.Z.N.A.® Total DNA kit (Omega Bio-tek, Norcross, GA, U.S.) manual for microbial community total DNA extraction, 1% agarose gel electrophoresis to detect DNA extraction quality, NanoDrop2000 to determine DNA concentration and purity; Use 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') to PCR amplify the V3-V4 variable region of the 16S rRNA gene. The amplification procedure is as follows: 95°C predenaturation 3 mins, 27 pieces Cycle (denaturation at 95°C for 30 s, annealing at 55°C for 30 s, extension at 72°C for 30 s), then stable extension at 72°C for 10 mins, and finally storage at 4°C (PCR instrument: ABI GeneAmp® 9700). The PCR reaction system is: 5×TransStart FastPfu buffer 4 µL, 2.5 mM dNTPs 2 µL, upstream primer (5uM) 0.8 µL, downstream primer (5uM) 0.8 µL, TransStart FastPfu DNA polymerase 0.4 µL, template DNA 10 ng, ddH2O Make up to 20 µL. 3 replicates for each sample. Subsequently, Illumina Miseq sequencing was performed: After mixing the PCR products of the same sample, use a 2% agarose gel to recover the PCR products. Use AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) to purify the recovered products and detect by 2% agarose gel electrophoresis. And use Quantus ™ Fluorometer (Promega, USA) to detect and quantify the recovered product. Use NEXTflexTM Rapid DNA-Seq Kit (Bioo Scientific, USA) to build the library: (1) linker linking; (2) use magnetic beads to screen to remove linker self-linked fragments; (3) use PCR amplification for library template enrichment; (4) The magnetic beads recover the PCR products to obtain the final library. Sequencing was performed using the Miseq PE300/NovaSeq PE250 platform of Illumina (Shanghai Meiji Biomedical Technology Co., Ltd.). Finally, use fastp[ 1 ] ( https://github.com/OpenGene/fastp , version 0.20.0) software for quality control of the original sequencing sequence, and use FLASH ( http://www.cbcb.umd.edu/software/flash , version 1.2.7) Software for splicing: (1) Filter the bases whose tail mass value is less than 20, and set a 50bp window. If the average quality value in the window is lower than 20, cut the back end from the window. Base, filter the reads below 50bp after quality control, remove the reads containing N bases; (2) According to the overlap relationship between PE reads, merge the paired reads into a sequence, the minimum overlap length is 10bp; (3) The maximum allowable mismatch ratio in the overlap area of the spliced sequence is 0.2, and the unmatched sequence is screened; (4) The samples are distinguished according to the barcodes and primers at the beginning and the end of the sequence, and the sequence direction is adjusted. The allowed number of mismatches in the barcode is 0, the maximum The number of primer mismatches is 2. Using UPARSE software ( http://drive5.com/uparse/ , version 7.1), the sequences were clustered by OTU based on the similarity of 97% and chimeras were eliminated. Use RDP classifier ( http://rdp.cme.msu.edu/ , version 2.2) to classify and annotate each sequence, compare with Silva 16S rRNA database (version 138), set the comparison threshold to 70% [ 33 ]. 6. Collection and processing of cervical exfoliated cells Cervical exfoliated cells were collected using a sterile cervical cell collection brush rotated in the same direction at the junction of the cervical squamous-column. The samples were immersed in a tube containing cell preservation solution, sealed, and sent to the laboratory of our hospital. Professional laboratory physicians used the second-generation hybrid capture detection method for detection and real-time fluorescent quantitative PCR for HPV genotyping (HC2-HPV-DNA test kit from Digene, USA, the operation method was carried out in strict accordance with the kit instructions, and the determination standard was: ≥500 copies/mL was positive, and < 500 copies /mL was negative).(High-risk HPV genotypes that the assay detects include: 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, 68). 7. Collection and processing of cervical biopsy tissue Colposcopy was performed by experienced senior physicians at our hospital. The patients were asked to take the lithotomy position to fully expose the vagina. Cervical morphology (color, surface structure, and vascular state) was observed. The cervix was soaked in acetic acid solution for 2 min, and smeared with iodine solution. The non-stained area was observed, and the biopsy site was determined. Using disposable cervical sampling clamps, the suspicious part of the cervical tissue was obtained. The specimen was fixed with 4% neutral formaldehyde fixative, and sent to the pathology department of our hospital for diagnosis. 8.Statistical analysis Xcalibur 2.2 was used to preprocess the original documents, and after internal normalization, PASW Statistics 23 (SPSS, Chicago, USA), SIMCA 13.0 (Umetrics, Umea, Sweden) and GraphPad Prism 8 (GraphPad Software Inc., San Diego, CA, USA) were used for clinical characteristics analysis, Principal Component Analysis (PCA), Orthogonal partial Least Squares Discrimination Analysis (OPLS-DA) analysis and differential metabolite analysis of the collected data, metabolite abundance analysis and metabolic pathway analysis of potential markers using metaboassay, the t-test of two independent samples was used for the comparison between the two groups of measurement data, and Mann Whitney U test was used for the diversity index, screening of metabolites by VIF variance expansion factor analysis. Results 1. Basic information such as HR-HPV and cervical biopsy pathology of patients The NILM group had an average age of 35.60 ± 7.843 years, with an average of 2.40 ± 1.761 pregnancies, and an average of 1.12 ± 0.581 births; the LSIL group had an average age of 37.50 ± 9.648 years, an average of 2.04 ± 1.414 pregnancies, and an average of 0.94 ± 0.639 births; the average age of the HSIL group was 38.08 ± 7.494 years, an average of 2.27 ± 1.343 pregnancies, and an average of 1.12 ± 0.711 births. There were no significant differences in age (F = 1.237, P = 0.293), pregnancy times (F = 0.798, P = 0.452), and parity (F = 1.336, P = 0.266) among the three groups of women. Among the 1recruited patients with HR-HPV infection, 9 were classified as monotype infections (59.6%), where the most frequent types were 16 (28 cases, 17.9%), 52 (12 cases, 7.7%), 18 (10 cases, 6.4%), and 58 (10 cases, 6.4%); 43 cases were double-type infections (27.6%); and 20 cases were multiple type of infections (12.8%). The pathological results of cervical biopsy under colposcopy showed that there were 78 cases of chronic cervicitis with squamous epithelial hyperplasia (NILM), 52 cases of LSIL, and 26 cases of HSIL. Statistics of routine indicators of leucorrhea in each group are shown in Table 1 . We speculate that the vaginal microecology of patients with HR-HPV infection increases and the cleanliness of the vaginal microenvironment decreases with increasing grade of cervical lesions. The imbalance in the vaginal flora may be related to the increase in the number of pathogenic anaerobes.(Cleanlines Degree I: A large number of vaginal epithelial cells and vaginobacteria were observed under the microscope. Degree II: Microscopically, vaginobacteria may be present, but there may be a few heterozygotes or pus cells. Degree III: Microscopically, there are a small number of bacteria, a large number of pus cells and heterobacteria. Degree IV: Colposis was not observed under the microscope, except for a few epithelial cells, mainly purulent cells and miscellaneous bacteria.) Table 1 Results of routine leucorrhea assessment in the different groups of patients. Group Number Cleanliness (n%) H 2 O 2 positive (n%) Sialidase positive (n%) Leukocyte esterase positive (n%) I-II° III° IV° NILM 78(50%) 47 (60.3%) 26 (33.3%) 5 (6.4%) 69 (88.5%) 19 (24.4%) 18 (23.1%) LSIL 52(33.3%) 25 (48.1%) 16 (30.8%) 11 (21.2%) 50 (89.3%) 14 (8.7%) 10 (17.9%) HSIL 26(16.7%) 8 (30.8%) 9 (34.6%) 9 (34.6%) 25 (96.2%) 9 (34.6%) 7 (26.9%) 2. Analysis of the bacterial community and principal component analysis of the samples We analyzed 1 samples for the bacterial composition in the vagina. At the genus level, a total of 200 microorganisms were common to the three groups. There were 149 species of unique to the NILM group, 86 to the LSIL group, and 73 to the HSIL group.As the degree of cervical lesions increased, the microbial diversity in the sample decreased slightly and then increased significantly. In the three groups of samples, we take the mean value of the species of the colony for comparison Lactobacillus was the most abundant, followed by Gardnerella . As the degree of cervical lesions increased, the abundance of Atopobium , Sneathia , and other bacteria increased, and the abundance of Streptococcus decreased (Fig. 1 ).The Alpha diversity of the three groups was compared by dilution curve. The results showed that the dilution curve of vaginal flora in HSIL group was obviously steep compared with that in the other two groups, and the dilution curve in LSIL group was slightly gentle than that in NILM group. Vaginal flora diversity may decrease first and then increase significantly. sobs index was used to compare the Alpha diversity of vaginal flora in the three groups. the results showed that there were significant differences between HSIL group and NILM group and LSIL group (Wilcoxon rank-sum test,P < 0.05)(Fig. 2 ).Comparing the beta diversity of three groups of samples through PCoA principal component analysis, the results showed that based on binary_lennon distance, HSIL group and the other two groups could be clearly distinguished (ANOSIM test, P 0.05)(Fig. 3 ). 3. Analysis of sample metabolites A total of 164 known metabolites were detected in the GC-MS spectra of 1 samples, and more than ten metabolites were found to be different between two groups, low abundances of several metabolites compared to NILM and HSIL(Fig. 4 ). Through the analysis of the peak values obtained from each group of metabolites, the metabolites that were different between the NILM group and the LSIL group in descending order were taurine, guanine, uric acid, xanthine, β-mannosylglycerate, tyramine, tyrosine, phenylacetaldehyde, adenine, purine, and so on. The metabolites that were different between the LSIL and HSIL groups in descending order were sucrose, sarcosine, pyrogallol, cetyl alcohol, γ-aminobutyric acid, stearyl alcohol, dioctyl phthalate, ketoisocaproic acid, uric acid, guanine, and taurine. The metabolites that were different metabolites between the LSIL and HSIL groups, in descending order, were phenylacetaldehyde, adenosine, α-aminoadipate, glucalditol, asparagine, cresol, palmitic acid, lyxono-1, 4-lactone, palmitoleic acid, hydroxyurea, talose, etc. Using the Mann-Whitney U test, peak of taurine was determined to be the metabolite that was the most significantly different between the NILM and LSIL groups (P < 0.0001), and between the LSIL group and HSIL group (P < 0.0001). Peak of guanine production was significantly different between the NILM and LSIL groups (P < 0.0001 ), and between the LSIL and HSIL groups (P = 0.0021). Peak of uric acid production was significantly different between the NILM and LSIL groups (P < 0.0001), and between the LSIL and HSIL groups (P = 0.0102) (Fig. 5 ). These results show that as the degree of cervical lesions deepens, amino acids, purines, sugars, and esters in the cervicovaginal microenvironment are consumed in large quantities. OPLS-DA was performed to determine the changes in the cervicovaginal metabolic patterns of HPV-infected patients with different types of cervical lesions. Figure 6 shows that the metabolic patterns in the HSIL patients were significantly different from those in the NILM and the LSIL patients. The OPLS-DA scores for the NILM and HSIL groups were R2Y = 0.765, Q2 = 0.407; and the OPLS-DA scores for the LSIL and HSIL groups were R2Y = 0.921, Q2 = 0.594.It is not observed that there is a tendency of separation of metabolic patterns between NILM and LSIL (R2Y < 0.5). 4. Influence of the vaginal flora on patients with HR-HPV infection After using MetaboAnalyst to analyze the metabolic pathways of the aforementioned potential markers, pathway topology analysis was performed to determine the relevant pathways with an impact value > 0.1. Four metabolic pathways correlated with LSIL, while three metabolic pathways correlated with HSIL (Fig. 7 ). 5. Correlation between the vaginal flora and metabolites The metabolites were screened by variance inflation factor (VIF) analysis, and clinical factors with small multicollinearity were retained for follow-up analysis. Factors with a VIF > 10 are generally considered useless. Therefore, metabolites with a VIF > 10 were filtered out first, and then the correlation coefficient between the metabolites and the top 50 abundant colony species were calculated through the correlation heatmap analysis. A variety of metabolites were significantly associated with the vaginal flora (Fig. 8 ). Taurine positively correlated with Weissella , Porphyromonas , and the Rikenellaceae RC9 gut group, and negatively correlated with Corynebacterium , Rhodococcus , and Ralstonia solanacearum . Hydroxyl products such as hydroxylamine, hydroxyurea, hydroxybutyric acid, and hydroxyphenyl propionic acid were significantly positively correlated with the various vaginal flora, but negatively correlated with Lactobacillus . Meanwhile, annitol, glycerol, guanine, adenine, sarcosine, tyrosine, threonine, etc. positively correlated with Lactobacillus . Discussion In this study, we found that cervicovaginal flora is closely associated with cervical cancer patients with high blood pressure and persistent HR-HPV infection. The vaginal flora of most HPV-infected women was still primarily composed of Lactobacillus and Gardnerella . However, with the increase of cervical lesion grade, the Alpha diversity of vaginal flora may decrease first and then increase significantly. The biological diversity of vaginal microecology in HSIL patients is significantly different from that in LSIL patients. The abundance of Atopobium and Sneathia increased and that of Streptococcus decreased with increasing grade of cervical lesions. This change in microbial abundance may affect the metabolic capacity of the microenvironment of the cervix and vagina. A healthy cervicovaginal microenvironment is primarily composed of slow-proliferating or quiescent cells. Changes in the cervicovaginal microbial composition caused by HPV infection may alter cellular metabolism to promote abnormal cell proliferation and protein synthesis. We detected a total of 164 known metabolites and found that taurine levels changed significantly in women at risk of cervical cancer. Other metabolites whose levels also significantly changed were mainly amino acids, purines, and sugars. Further pathway analysis also found 7 metabolic pathways that may be related to it, including aminoacyl-tRNA biosynthesis pathway, phenylalanine and tyrosine and tryptophan pathways, alanine and aspartic acid and glutamine Acid metabolism pathway, d-glutamine and d-glutamate metabolism pathway, arginine biosynthesis pathway, β-alanine metabolism pathway, phenylalanine metabolism pathway.A variety of metabolites were significantly associated with the vaginal flora. Taurine positively correlated with Weissella , Porphyromonas , and other miscellaneous bacteria, and negatively correlated with Corynebacterium , Rhodococcus , and Ralstonia solanacearum . Lactobacillus is positively correlated with purines and amino acids, and negatively correlated with hydroxyl products. The massive consumption of key amino acid metabolites involved in cell growth, such as taurine, indicates that only HPV-positive and SIL patients’ healthy cell metabolism is disrupted, which is a potential functional consequence of HPV-induced cervical-vaginal dystrophy [ 35 ]. Taurine is a rare amino acid and the most abundant free amino acid in the heart, retina, skeletal muscle, and white blood cells. It plays a vital role in energy metabolism [ 25 ] and has also been shown to protect tissues in several oxidative-damage animal models. It can be converted into taurine chloramine, which is more stable and less toxic. Taurine chloramine acts a potent regulator of the mouse and human immune system by downregulating the production of pro-inflammatory mediators in leukocytes. It can also inhibit the activation of NF-κB, an effective signal transduction factor for inflammatory cytokines [ 26 ]. The latest research shows that in the process of exploring the body's natural defenses against bacterial infections, it was found that low levels of taurine can cause pathogenic bacteria to colonize the intestines, and high levels of taurine can produce enough hydrogen sulfide to prevent disease. Taurine can be prepared to prepare the intestinal flora to prevent infection. In this study, taurine was significantly reduced in LSIL and significantly increased in HSIL, indicating that taurine is related to the defensive effect of cervicovaginal flora [ 34 ]. Recent research on the body's natural defenses against bacterial infections has shown that low levels of taurine can cause pathogenic bacteria to colonize the intestines, and high levels of taurine can lead to production of enough hydrogen sulfide to prevent disease. In this study, taurine level was significantly reduced in LSIL and significantly increased in HSIL, indicating that it is related to the defense function of the cervicovaginal flora [ 31 ]. Guanine has been shown to regulate the enteric nervous system [ 27 ], and the oxidative damage caused by 7,8-dihydro-8-oxoguanine, a derivative of guanine, is also significantly related to inflammation [ 28 ]. Uric acid is a heterocyclic purine derivative and the final oxidation product of purine metabolism. Ryu et al. reported that uric acid can reduce the expression of E-cadherin in rat renal tubular epithelial cells, thereby disrupting the ability of epithelial cells to regulate factors such as nitric oxide that are needed to increase renal blood flow [ 29 ]. In addition, uric acid crystals can activate innate host defense mechanisms by changing membrane lipid composition, triggering inflammation, and activating immune responses [ 30 ].Metabolic abnormalities such as amino acids, purines, and uric acid prove that HR-HPV patients have enhanced inflammatory response and suppressed immune defense response. Compared with the NILM group, uric acid was down-regulated in the HSIL group, and there was no statistical difference between taurine and guanine between the two groups. Compared with the NILM group, taurine, guanine, and uric acid were significantly down-regulated in the LSIL group; compared with LSIL in the HSIL group, they were significantly up-regulated, which proves that the initial progression of cervical lesions will consume a large amount of amino acids, purines, etc. in the cervix-vaginal environment. As the disease level increases, amino acids, purines and other metabolites may produce compensatory up-regulation and participate in the immune defense function of cervical and vaginal epithelial cells. Taken together, the cervicovaginal flora may play a major role in activating inflammation and the immune response by regulating the synthesis and decomposition of amino acids, esters, purines, and lipids. Using the multi-omics combination of analyzing the microbiome and its metabolome, we can infer the types of bacteria that change the characteristic metabolites that leads to cervical vaginal inflammation and progression of cervical precancerous lesions. Based on this information, we can consider the risk of cancer progression by exploring changes in these metabolites. The method we presented here provides a new diagnostic strategy for preventing and preventing precancerous lesions in patients with HPV infection. For the first time, taurine was found in vaginal lavage fluid samples in this study and can be a potential biomarker for cervical cancer. However, this pilot study did not consider interfering factors such as age, BMI, STIs, vaginal disorders (BV and AV), regional and personal lifestyle differences; thus, a larger sample size is needed for further verification of the results as well as a higher number of cervical cancer patients and stricter inclusion criteria for analysis and comparison. If verified in larger studies, the metabolites that have been correlated with cervical precancerous lesions and cervical cancer (taurine, guanine, and uric acid) in this study may be used as one of clinical diagnostic markers in the future. Declarations Ethical statement The experimental study comply with relevant privacy protection laws and guidelines. This study has obtained ethical approval from Ethics Committee of Affiliated Hospital of Nanjing University of Chinese Medicine with approval number 2021NL-025-03. author contributions SS: Conceptualization, Investigation, Writing - Original Draft, Visualization SZ: Data Curation, Investigation JS: Validation, Supervision QR: Writing - Review & Editing, Project administration data availability statement 16s gene sequencing data uploaded to NCBI database, BioProject: PRJNA722355 Metabolomics data that supports the findings of this study are available in the supplementary material of this article. Conflict of Interest All authors disclosed no relevant realtionships. References Wei Lihui, ZHAO Yun, SHEN Danhua, et al. Chinese Expert Consensus on cervical cancer screening and abnormal management (I)[J]. Chinese Journal of Obstetrics and Gynecology,2017,(02):190–192. Wei Lihui, SHEN Danhua, ZHAO Fanghui, et al. Expert consensus on cervical cancer screening and abnormal management in China (II)[J]. Chinese Journal of Clinical Obstetrics and Gynecology, 2017(03):99–101. Martin. The application of precision medicine in the prevention and treatment of cervical cancer [J]. Journal of Applied Obstetrics and Gynecology,2017,33(06):403–406. Global Burden of Disease Cancer Collaboration,Fitzmaurice C,Allen C,et al. Global,Regional,and National Cancer Incidence,Mortality,Years of Life Lost,Years Lived With Disability,and Disability-Adjusted Life-years for 32 Cancer Groups,1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study[J]. JAMA Oncol,2017,3(4):524–548. Shang-Ying Hu, Remila Rezhake, Feng Chen, Xun Zhang, Qin-Jing Pan, Jun-Fei Ma, You-Lin Qiao, Fang-Hui Zhao. Outcomes in women with biopsy-confirmed cervical intraepithelial neoplasia grade 1 or normal cervix and related cofactors: A 15-year population-based cohort study from China.[J]. Gynecologic Oncology, 2020, 156(3):616–623. Zhu Ruo-xi, HAO Min, ZHAO Wei-hong, WANG Wei, WANG Zh-lian, WANG Jin-tao, FENG Bo, Yang Jing, WANG Zhe, NIU Xiao-fen. Relationship between changes in vaginal microbiological metabolites and the risk of high-risk human papillomavirus infection and cervical intraepithelial neoplasia [J]. Chinese Journal of Practical Gynecology and Obstetrics, 2019, 35(07):797–802. Champer M, Wong AM, Champer J, Brito IL, Messer PW, Hou JY, Wright JD. The role of the vaginal microbiome in gynaecological cancer.[J]. Bjog, 2018, 125(3): 309–315. Dahoud W, Michael CW, Gokozan H, Nakanishi AK, Harbhajanka A. Association of Bacterial Vaginosis and Human Papilloma Virus Infection With Cervical Squamous Intraepithelial Lesions.[J]. Am. J. Clin. Pathol., 2019, 152(2): 185–189. Scott M, Stites DP and Moscicki AB: Th1 cytokine patterns in cervical human papillomavirus infection. Clin Diagn Lab Immunol 6: 751–755, 1999. Shannon B, Yi TJ, Perusini S, Gajer P, Ma B, Humphrys MS, Thomas-Pavanel J, Chieza L, Janakiram P, Saunders M, Tharao W, Huibner S, Shahabi K, Ravel J, Rebbapragada A, Kaul R. Association of HPV infection and clearance with cervicovaginal immunology and the vaginal microbiota. Mucosal Immunol. 2017 Sep;10(5):1310–1319. Shannon B, Yi TJ, Perusini S, Gajer P, Ma B, Humphrys MS, Thomas-Pavanel J, Chieza L, Janakiram P, Saunders M, Tharao W, Huibner S, Shahabi K, Ravel J, Rebbapragada A, Kaul R. Association of HPV infection and clearance with cervicovaginal immunology and the vaginal microbiota. Mucosal Immunol. 2017 Sep;10(5):1310–1319. Champer M, Wong AM, Champer J, Brito IL, Messer PW, Hou JY, Wright JD. The role of the vaginal microbiome in gynaecological cancer.[J]. Bjog, 2018, 125(3): 309–315. Petrova MI, Lievens E, Malik S, Imholz N, Lebeer S. Lactobacillus species as biomarkers and agents that can promote various aspects of vaginal health. Front Physiol. 2015 Mar 25;6:81. Brotman RM, Shardell MD, Gajer P, Tracy JK, Zenilman JM, Ravel J, Gravitt PE. Interplay between the temporal dynamics of the vaginal microbiota and human papillomavirus detection.[J]. J Infect Dis, 2014, 210 (11):1723–1733. Łaniewski P, Barnes D, Goulder A, Cui H, Roe DJ, Chase DM, Herbst-Kralovetz MM. Linking cervicovaginal immune signatures, HPV and microbiota composition in cervical carcinogenesis in non-Hispanic and Hispanic women. Sci Rep. 2018 May 15;8(1):7593. Chao XP, Sun TT, Wang S, Fan QB, Shi HH, Zhu L, Lang JH. Correlation between the diversity of vaginal microbiota and the risk of high-risk human papillomavirus infection.[J]. Int J Gynecol Cancer, 2019, 29(1): 28–34. Pan Ying, SHENG Huafang, KANG Ling, Ma Xiao, ZHENG Huimin, Zhou Hongwei, LIU Mubiao. Study on the correlation between high risk human papillomavirus infection and vaginal microflora [J]. Journal of the Third Military Medical University,2016,38(13): 1559–1564. Kaji R, Kiyoshima-Shibata J, Tsujibe S, Nanno M, Shida K. Short communication: Probiotic induction of interleukin-10 and interleukin-12 production by macrophages is modulated by co-stimulation with microbial components.[J]. J. Dairy Sci, 2018, 101(4): 2838–2841. Oh HY, Kim BS, Seo SS, Kong JS, Lee JK, Park SY, Hong KM, Kim HK, Kim MK. The association of uterine cervical microbiota with an increased risk for cervical in- traepithelial neoplasia in Korea.[J]. Clin Microbiol In- fect, 2015, 21(7): 674.e1-9. Sanchooli A, Aghaiypour K, Kiasari BA, Samarbaf-Zadeh A, Ghadiri A, Makvandi M. VLP Production from Recombinant L1/L2 HPV-16 Protein Expressed in Pichia Pastoris.[J]. Protein Pept. Lett, 2018, 25(8): 783–790. Mitra A, MacIntyre DA, Lee YS, et al. Cervical intraepithelial neoplasia disease progression is associated with increased vaginal microbiome diversity. Sci Rep. 2015;5:16865. Published 2015 Nov 17. Torcia MG. Interplay among Vaginal Microbiome, Immune Response and Sexually Transmitted Viral Infections. Int J Mol Sci. 2019;20(2):266. Published 2019 Jan 11. doi: 10.3390/ijms20020266 Brusselaers, N.; Shrestha, S.; Van de Wijgert, J.; Verstraelen, H. Vaginal dysbiosis and the risk of human papillomavirus and cervical cancer: Systematic review and meta-analysis. Am. J. Obstet. Gynecol. 2019, 221, 9–18. Srinivasan S, Morgan MT, Fiedler TL, Djukovic D, Hoffman NG, Raftery D, Marrazzo JM, Fredricks DN. Metabolic signatures of bacterial vaginosis. mBio. 2015 Apr 14;6(2):e00204-15. Wen C, Li F, Zhang L, et al. Taurine is Involved in Energy Metabolism in Muscles, Adipose Tissue, and the Liver. Mol Nutr Food Res. 2019;63(2):e1800536. doi: 10.1002/mnfr.201800536 Schuller-Levis GB, Park E. Taurine and its chloramine: modulators of immunity. Neurochem Res. 2004;29(1):117–126. doi: 10.1023/b:nere.0000010440.37629.17 . Zizzo MG, Mulè F, Mastropaolo M, Condorelli DF, Belluardo N, Serio R. Can guanine-based purines be considered modulators of intestinal motility in rodents?. Eur J Pharmacol. 2011;650(1):350–355. Visnes T, Cázares-Körner A, Hao W, et al. Small-molecule inhibitor of OGG1 suppresses proinflammatory gene expression and inflammation. Science. 2018;362(6416):834–839. doi: 10.1126/science.aar8048 Giordano C, Karasik O, King-Morris K, Asmar A. Uric Acid as a Marker of Kidney Disease: Review of the Current Literature. Dis Markers. 2015;2015:382918. doi: 10.1155/2015/382918 . Ng G, Chau EM, Shi Y. Recent developments in immune activation by uric acid crystals. Arch Immunol Ther Exp (Warsz). 2010;58(4):273–277. doi: 10.1007/s00005-010-0082-1 . Stacy A, Andrade-Oliveira V, McCulloch JA, Hild B, Oh JH, Perez-Chaparro PJ, Sim CK, Lim AI, Link VM, Enamorado M, Trinchieri G, Segre JA, Rehermann B, Belkaid Y. Infection trains the host for microbiota-enhanced resistance to pathogens. Cell. 2021 Feb 4;184(3):615–627.e17. Chen Y, Qiu X, Wang W, et al. Human papillomavirus infection and cervical intraepithelial neoplasia progression are associated with increased vaginal microbiome diversity in a Chinese cohort. BMC Infect Dis. 2020;20(1):629. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007 Aug;73(16):5261–7. Apollo Stacy et al. Infection trains the host for microbiota-enhanced resistance to pathogens. Cell, 2021, doi: 10.1016/j.cell.2020.12.011 . Ilhan ZE, Łaniewski P, Thomas N, Roe DJ, Chase DM, Herbst-Kralovetz MM. Deciphering the complex interplay between microbiota, HPV, inflammation and cancer through cervicovaginal metabolic profiling. EBioMedicine. 2019;44:675–690. Additional Declarations No competing interests reported. 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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-3066333","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":215980748,"identity":"013b44e1-731a-4f47-9e92-0e9a4b47d7e4","order_by":0,"name":"Su Shen","email":"","orcid":"","institution":"University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Su","middleName":"","lastName":"Shen","suffix":""},{"id":215980749,"identity":"1c4c7313-21a5-4221-9515-ef46c34a5fef","order_by":1,"name":"Shixian Zhao","email":"","orcid":"","institution":"University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shixian","middleName":"","lastName":"Zhao","suffix":""},{"id":215980750,"identity":"acfdd70d-a9e8-45e4-aad9-1a21c0d4422e","order_by":2,"name":"Jinjun Shan","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinjun","middleName":"","lastName":"Shan","suffix":""},{"id":215980751,"identity":"fc8068a7-ecc1-4a87-b352-edf7228243cb","order_by":3,"name":"Qingling Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3RPQrCMBiA4ZRAdEh1rdSqR0gJ1EU8S0WwSzqIi2PBVZz1FnEStw8KdekB3OzkLLpb69/a6CaYFwIpfA8JKUI63S+GAYFP8xZC8Pg2IiUhvpGdmph/RbC77OHBe1JNWEI9mwoSbIzUPe9Rz5GAj5mapDTcRim3BRpxCaTLSslh7tnm3AolpB4WKB5IoMRSnmJeWcCeJP+IcHdJff9FQE0aCRlnJwquhGRiCzbkq5h4paSWYFn8Smizfby+iGnfWexmx1LSgerreSy437NYuGy+qB1VsueuHilGdTqd7m+7Adg8TgN8hEFkAAAAAElFTkSuQmCC","orcid":"","institution":"University of Chinese Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Qingling","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2023-06-15 07:29:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3066333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3066333/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":39857360,"identity":"a9b1ac20-34f2-41fc-a7e3-f354452d705d","added_by":"auto","created_at":"2023-07-11 14:18:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":885666,"visible":true,"origin":"","legend":"\u003cp\u003eThe number and composition of microorganisms in the three NILM, LSIL, and HSIL groups.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3066333/v1/aefc08fdfc9dfc63f959457b.png"},{"id":39855798,"identity":"5e49accd-636e-4a74-b5b9-994bbd3d6970","added_by":"auto","created_at":"2023-07-11 14:10:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121237,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Alpha diversity index between NILM, LSIL, and HSIL groups.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3066333/v1/d72d0574148e2aeaa5bd998b.png"},{"id":39855799,"identity":"f0a8de3c-657f-4ced-96c6-9ba2de63c389","added_by":"auto","created_at":"2023-07-11 14:10:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116348,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Beta diversity index between NILM, LSIL, and HSIL groups.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3066333/v1/08a48e2d8e69e6cf89af0c02.png"},{"id":39855805,"identity":"797db712-54af-4313-8deb-dfb86bf167cc","added_by":"auto","created_at":"2023-07-11 14:10:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1643074,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of the 50 most abundant metabolites found in the three groups of samples.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3066333/v1/e373296730ac73a9e7f036a1.png"},{"id":39858389,"identity":"ddea6e23-703b-4460-b69e-8bd5bd4f4897","added_by":"auto","created_at":"2023-07-11 14:26:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":173330,"visible":true,"origin":"","legend":"\u003cp\u003ePeak changes in the taurine, guanine, and uric acid levels among different groups.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3066333/v1/bc03ad32b8965ecc0e2da0b9.png"},{"id":39857362,"identity":"b7902764-5c73-46f7-8616-5e504fd34d3f","added_by":"auto","created_at":"2023-07-11 14:18:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":251825,"visible":true,"origin":"","legend":"\u003cp\u003eOPLS-DA of cervicovaginal metabolites in the HSIL group compared with those in the (A,B) NILM and (C,D) LSIL groups.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3066333/v1/890eec082ad2308ee3e96023.png"},{"id":39855803,"identity":"6a29c39b-2a27-4090-aa00-d3c8523b7905","added_by":"auto","created_at":"2023-07-11 14:10:34","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":111472,"visible":true,"origin":"","legend":"\u003cp\u003eThe metabolic pathways through which the cervicovaginal flora affects the occurrence of LSIL and HSIL. (A) The pathways that mainly affect LSIL are: (a) aminoacyl-tRNA biosynthesis; (b) phenylalanine, tyrosine, and tryptophan biosynthesis; (c) alanine, aspartate, and glutamate metabolism; (d) D-Glutamine and D-glutamate metabolism; and (e) Arginine biosynthesis. (B) The pathways that mainly affect HSIL include: (a) aminoacyl-tRNA biosynthesis; (b) phenylalanine, tyrosine, and tryptophan biosynthesis; (c) alanine, aspartate, and glutamate metabolism; (d). beta-alanine metabolism; and (e)phenylalaninemetabolism.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3066333/v1/e3e0aacbe4bf072c54356500.png"},{"id":39855801,"identity":"750bef68-8c5e-4ef4-b94b-b0acf7b5b599","added_by":"auto","created_at":"2023-07-11 14:10:34","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":312928,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of the correlation between the vaginal flora and sample metabolites.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3066333/v1/7bb887857dd1ba025f1cbc39.png"},{"id":41908534,"identity":"19c07e95-f9f8-4516-ba30-f18299bfb98e","added_by":"auto","created_at":"2023-08-22 03:52:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3634506,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3066333/v1/17835442-88c8-4962-ad5d-9060bf9aed91.pdf"},{"id":39855806,"identity":"c70c2601-35b7-4848-a6df-906279c3b330","added_by":"auto","created_at":"2023-07-11 14:10:34","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4341169,"visible":true,"origin":"","legend":"","description":"","filename":"Metabolomicsdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3066333/v1/934734ea1a482b0229daaa61.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlations between microbiota-derived metabolites and cervical precancerous lesions in women with HPV","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer is one of the most common gynecological malignancies, with a high incidence rate and a youthful trend [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Cervical cancer has become a major social problem that threatens women\u0026rsquo;s health and quality of life. Cervical squamous intraepithelial lesion (SIL) is a common disease that endangers women\u0026rsquo;s health. It is a group of cervical lesions that are closely related to invasive cervical cancer. High-risk human papillomavirus (HR-HPV) was found in nearly 90% of cervical SIL and 99% of cervical cancer tissues by a 15-year population-based cohort study from China[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Current studies have shown that an imbalance in the vaginal flora is associated with bacterial vaginosis and infectious diseases. The outcome of HPV infection has been associated with both host and viral factors, and with balance in the vaginal microenvironment [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Several studies have confirmed that an imbalance in the cervicovaginal flora may lead to persistent HR-HPV infection and may be involved in the development of SIL [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the mechanisms remain unclear and require further understanding.\u003c/p\u003e \u003cp\u003eAccording to published reports, HPV infection affects mucosal metabolism, host immune status, and can cause changes in the vaginal microbiota [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the converse is true as vaginal microbiota, host immunity can also have an impact on HPV infection [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. HPV-infected individuals have a highly diverse cervicovaginal microbiome [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. HPV infection and clearance rates are related to the increased Langerhans cells and the composition of the vaginal microbiome dominated by \u003cem\u003eLactobacillus geeseri\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The persistence of HR-HPV infection has been correlated with an imbalance in the vaginal microecology and with damage to the cervicovaginal mucosal barrier [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A myriad of bacteria in the cervicovaginal environment, with \u003cem\u003eLactobacillus species\u003c/em\u003e, thought to be biomarkers for cervicovaginal health, being frequently identified [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. They coordinate with and restrict each other to form the vaginal micro-ecosystem. When the host's body changes, the vaginal microflora will also change in composition, breaking the original balance and causing a variety of inflammations and viral infections. In a 16-week cohort study, the composition of the vaginal flora of HPV-positive women was found to be dominated by \u003cem\u003eLactobacillus iners\u003c/em\u003e and anaerobic bacteria. Additionally, women with vaginal CST IV had an HPV positivity rate of 72% [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Another study has shown that \u003cem\u003eLactobacillus indolent\u003c/em\u003e may play a non-beneficial role in persistent HPV infection. Compare to women with \u003cem\u003eLactobacillus crispatus\u003c/em\u003e, HPV infection has been reported to have lower clearance rates and persisted longer in women with \u003cem\u003eLactobacillus inert\u003c/em\u003e [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The certain cervicovaginal bacteria, including \u003cem\u003eLactobacillus\u003c/em\u003e though with differing level of protection, have been associated with the natural history of HPV infection [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Some studies have reported that ciliates and \u003cem\u003eBrucella\u003c/em\u003e in the vagina are closely linked to HR-HPV infection [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies have also found that \u003cem\u003eLactobacillus\u003c/em\u003e stimulates the phagocytic ability of host phagocytes and cells to produce various cytokines such as interleukin-10 (IL-10), IL-12, interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), etc. to enhance the immune capacity of the host [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A study has also shown that in the case of women lacking \u003cem\u003eL. crispatus\u003c/em\u003e, the competitive proliferation of three bacterial groups, \u003cem\u003eL. iners\u003c/em\u003e, \u003cem\u003eGardnerella vaginalis\u003c/em\u003e, and \u003cem\u003eAnaerococcus vaginalis\u003c/em\u003e, leads to the development of SIL [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Some studies have shown that \u003cem\u003eLactobacillus\u003c/em\u003e has a plasmid-like function, which can integrate HPV16 gene, thereby preventing the virus from integrating with host cells [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The loss of the dominant position of Lactobacillus will promote the colonization of anaerobic bacterial species and increase the diversity of microorganisms. This usually causes changes in immunity and epithelial homeostasis through a variety of mechanisms, which in turn promotes HPV infection [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Chronic mucosal inflammation is also considered to be the core of HPV-induced cervical cancer [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Chronic inflammation of the cervix and vagina can cause cervical epithelial degeneration and necrosis, leading to the destruction of the cervical epithelial barrier, which triggers an immune response. This also makes the cervix and vagina susceptible to the reproduction of other pathogenic microorganisms, thereby contributing to the persistence of HPV and ultimately promoting the occurrence of SIL [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies have shown that different metabolites produced by different bacterial groups may have disease-related or protective roles. Metabolites of cervical microorganisms may enter systemic circulation and impact the overall health of high-risk cervical cancer patients [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTaken together, these studies show that an imbalance of the vaginal flora not only impairs immune responses but also promotes the persistence of HR-HPV infection and the progression of SIL. The changes in metabolites caused by the vaginal flora may reveal risks of cervical cancer patients with HR-HPV infection [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Cervical lavage fluid is derived from secretions of the cervicovaginal cavity. Collection of the cervical lavage fluid is easy and non-invasive as compared to the collection of cervix exfoliated cells. Thus, it may serve as an accessible tool for monitoring biomarkers to assess the risks of cervical cancer in women with HR-HPV infection. Therefore, in this study, we collected cervicovaginal lavage fluid samples from women with HPV infection with different degrees of cervical lesions. We identified the corresponding changes to the microflora and its metabolites to mine for potential biomarkers that can be used to predict the risk of cervical cancer. The findings we present here provide insights into diagnostic and monitoring approaches to help prevent the development of cervical cancer in women with HPV infection.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e1. Subjects of the study\u003c/p\u003e \u003cp\u003eFrom May to July 2020, a total of 156 women with HR-HPV were recruited from the Department of Gynecology, Jiangsu Hospital of Traditional Chinese Medicine. Patients whose thinprep cytologic test (TCT) showed atypical squamous cells of undetermined significance (ASCUS) and above lesions need colposcopy and cervical biopsy. All the patients knowingly and voluntarily cooperated with the examination. The patients were divided into groups based on the results of the cervical biopsy: no intraepithelial cell lesions (NILM group), low-grade cervical squamous intraepithelial lesions (LSIL group), and high-grade cervical squamous intraepithelial lesions (HSIL group).\u003c/p\u003e \u003cp\u003e2. Inclusion and exclusion criteria\u003c/p\u003e \u003cp\u003eThe inclusion criteria were: (1)\u0026thinsp;\u0026ge;\u0026thinsp;30 years of age, non-menopausal women; (2) sexual history, regular menstruation and the menstrual period was not reached when the sample was taken; (3) patients without BV, AV, STI (such as chlamydia) and not using hormonal contraception; (4) complete clinical data.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were: (1) total hysterectomy; (2) history of immune-related diseases or use of immunosuppressive agents; (3) major diseases such as those of the heart, lung, and kidney; (4) medication for cervical disease within 1 month prior to the study; (5) pregnancy or breastfeeding.\u003c/p\u003e \u003cp\u003e3. Collection of cervicovaginal secretions and cervicovaginal lavage fluid\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003ewas obtained before sample collection, The patients did not use suppositories or flushing drugs within the first three days of sample collection and had no sexual activity within 48 hours before collection. A sterile cotton swab was used to collect the secretions at the cervix and a third of the vaginal side wall of patients in the lithotomy position. The swabs were then placed in a sterile test tube for routine leucorrhea detection within 15 min. The cervix and the upper a third of the vaginal wall were washed with 5 mL 0.9% sodium chloride solution, and 2 mL of the lavage fluid was withdrawn. The lavage fluid samples centrifuged at 4\u0026deg;C, 18,000 rpm for 10 min, and the supernatant and precipitate were stored at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e4. Metabolomic analysis of cervicovaginal lavage samples\u003c/p\u003e \u003cp\u003eThe supernatant of the lavage samples were used for metabolomic analysis. The supernatant was made into lyophilized supernatant. Lyophilized supernatants of the lavage samples (500 \u0026micro;L) were reconstituted with 100\u0026micro;L sterile distilled water. Then, to each sample, 12.5 \u0026micro;g/mL ice-cold methanol solution containing myristic acid (400 \u0026micro;L) was added. The solutions were vortexed for 3 min and centrifuged for 10 mins (4℃, 18,000 rpm). Then, 200\u0026micro;L of the supernatant was transferred to a centrifugal concentrator to evaporate for 2 h. Thirty microliters of 10mg/mL methoxyamine pyridine was added. The samples were then vortexed for 5 mins and incubated at 30\u0026deg;C for 1.5 h with shaking (450 rpm). Next, 30 \u0026micro;L N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) was added to the samples; the samples were vortexed for 5 mins and incubated 37\u0026deg;C for 0.5 h with shaking (450 rpm). The samples were then centrifuged for 10 min (4\u0026deg;C, 18,000 rpm), and 50 \u0026micro;L of the supernatant was used for metabolite analysis by trace 1310 gas chromatography and TSQ 8000 mass spectrometer equipped with AS 1310 automatic injector; Tg-5ms Gas Chromatography Column (0.25 mm * 30 m, 0.25 um) (GC-MS, Thermo, Waltham, USA). At the same time, 3 \u0026micro;L supernatant was taken from all samples and the same amount was mixed to prepare quality control (QC) samples for methodological verification. After running a sequence of 10 samples, one QC sample was analyzed to evaluate the applicability of the system and the stability of the system. The obtained original file was preprocessed with Xcalibur 2.2 (Thermo, Waltham, USA) and normalized using the internal standard to obtain the metabolite name and corresponding peak value.\u003c/p\u003e \u003cp\u003e5. DNA extraction, 16S rRNA sequencing, and operational taxonomic unit (OTU) clustering\u003c/p\u003e \u003cp\u003eAccording to the E.Z.N.A.\u0026reg; Total DNA kit (Omega Bio-tek, Norcross, GA, U.S.) manual for microbial community total DNA extraction, 1% agarose gel electrophoresis to detect DNA extraction quality, NanoDrop2000 to determine DNA concentration and purity; Use 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') to PCR amplify the V3-V4 variable region of the 16S rRNA gene. The amplification procedure is as follows: 95\u0026deg;C predenaturation 3 mins, 27 pieces Cycle (denaturation at 95\u0026deg;C for 30 s, annealing at 55\u0026deg;C for 30 s, extension at 72\u0026deg;C for 30 s), then stable extension at 72\u0026deg;C for 10 mins, and finally storage at 4\u0026deg;C (PCR instrument: ABI GeneAmp\u0026reg; 9700). The PCR reaction system is: 5\u0026times;TransStart FastPfu buffer 4 \u0026micro;L, 2.5 mM dNTPs 2 \u0026micro;L, upstream primer (5uM) 0.8 \u0026micro;L, downstream primer (5uM) 0.8 \u0026micro;L, TransStart FastPfu DNA polymerase 0.4 \u0026micro;L, template DNA 10 ng, ddH2O Make up to 20 \u0026micro;L. 3 replicates for each sample. Subsequently, Illumina Miseq sequencing was performed: After mixing the PCR products of the same sample, use a 2% agarose gel to recover the PCR products. Use AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) to purify the recovered products and detect by 2% agarose gel electrophoresis. And use Quantus\u003csup\u003e\u0026trade;\u003c/sup\u003e Fluorometer (Promega, USA) to detect and quantify the recovered product. Use NEXTflexTM Rapid DNA-Seq Kit (Bioo Scientific, USA) to build the library: (1) linker linking; (2) use magnetic beads to screen to remove linker self-linked fragments; (3) use PCR amplification for library template enrichment; (4) The magnetic beads recover the PCR products to obtain the final library. Sequencing was performed using the Miseq PE300/NovaSeq PE250 platform of Illumina (Shanghai Meiji Biomedical Technology Co., Ltd.). Finally, use fastp[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/OpenGene/fastp\u003c/span\u003e\u003cspan address=\"https://github.com/OpenGene/fastp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 0.20.0) software for quality control of the original sequencing sequence, and use FLASH (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbcb.umd.edu/software/flash\u003c/span\u003e\u003cspan address=\"http://www.cbcb.umd.edu/software/flash\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 1.2.7) Software for splicing: (1) Filter the bases whose tail mass value is less than 20, and set a 50bp window. If the average quality value in the window is lower than 20, cut the back end from the window. Base, filter the reads below 50bp after quality control, remove the reads containing N bases; (2) According to the overlap relationship between PE reads, merge the paired reads into a sequence, the minimum overlap length is 10bp; (3) The maximum allowable mismatch ratio in the overlap area of the spliced sequence is 0.2, and the unmatched sequence is screened; (4) The samples are distinguished according to the barcodes and primers at the beginning and the end of the sequence, and the sequence direction is adjusted. The allowed number of mismatches in the barcode is 0, the maximum The number of primer mismatches is 2. Using UPARSE software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://drive5.com/uparse/\u003c/span\u003e\u003cspan address=\"http://drive5.com/uparse/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 7.1), the sequences were clustered by OTU based on the similarity of 97% and chimeras were eliminated. Use RDP classifier (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rdp.cme.msu.edu/\u003c/span\u003e\u003cspan address=\"http://rdp.cme.msu.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 2.2) to classify and annotate each sequence, compare with Silva 16S rRNA database (version 138), set the comparison threshold to 70% [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e6. Collection and processing of cervical exfoliated cells\u003c/p\u003e \u003cp\u003eCervical exfoliated cells were collected using a sterile cervical cell collection brush rotated in the same direction at the junction of the cervical squamous-column. The samples were immersed in a tube containing cell preservation solution, sealed, and sent to the laboratory of our hospital. Professional laboratory physicians used the second-generation hybrid capture detection method for detection and real-time fluorescent quantitative PCR for HPV genotyping (HC2-HPV-DNA test kit from Digene, USA, the operation method was carried out in strict accordance with the kit instructions, and the determination standard was: \u0026ge;500 copies/mL was positive, and \u0026lt;\u0026thinsp;500 copies /mL was negative).(High-risk HPV genotypes that the assay detects include: 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, 68).\u003c/p\u003e \u003cp\u003e7. Collection and processing of cervical biopsy tissue\u003c/p\u003e \u003cp\u003eColposcopy was performed by experienced senior physicians at our hospital. The patients were asked to take the lithotomy position to fully expose the vagina. Cervical morphology (color, surface structure, and vascular state) was observed. The cervix was soaked in acetic acid solution for 2 min, and smeared with iodine solution. The non-stained area was observed, and the biopsy site was determined. Using disposable cervical sampling clamps, the suspicious part of the cervical tissue was obtained. The specimen was fixed with 4% neutral formaldehyde fixative, and sent to the pathology department of our hospital for diagnosis.\u003c/p\u003e \u003cp\u003e8.Statistical analysis\u003c/p\u003e \u003cp\u003eXcalibur 2.2 was used to preprocess the original documents, and after internal normalization, PASW Statistics 23 (SPSS, Chicago, USA), SIMCA 13.0 (Umetrics, Umea, Sweden) and GraphPad Prism 8 (GraphPad Software Inc., San Diego, CA, USA) were used for clinical characteristics analysis, Principal Component Analysis (PCA), Orthogonal partial Least Squares Discrimination Analysis (OPLS-DA) analysis and differential metabolite analysis of the collected data, metabolite abundance analysis and metabolic pathway analysis of potential markers using metaboassay, the t-test of two independent samples was used for the comparison between the two groups of measurement data, and Mann Whitney U test was used for the diversity index, screening of metabolites by VIF variance expansion factor analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e1. Basic information such as HR-HPV and cervical biopsy pathology of patients\u003c/p\u003e \u003cp\u003eThe NILM group had an average age of 35.60\u0026thinsp;\u0026plusmn;\u0026thinsp;7.843 years, with an average of 2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.761 pregnancies, and an average of 1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.581 births; the LSIL group had an average age of 37.50\u0026thinsp;\u0026plusmn;\u0026thinsp;9.648 years, an average of 2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.414 pregnancies, and an average of 0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.639 births; the average age of the HSIL group was 38.08\u0026thinsp;\u0026plusmn;\u0026thinsp;7.494 years, an average of 2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.343 pregnancies, and an average of 1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.711 births. There were no significant differences in age (F\u0026thinsp;=\u0026thinsp;1.237, P\u0026thinsp;=\u0026thinsp;0.293), pregnancy times (F\u0026thinsp;=\u0026thinsp;0.798, P\u0026thinsp;=\u0026thinsp;0.452), and parity (F\u0026thinsp;=\u0026thinsp;1.336, P\u0026thinsp;=\u0026thinsp;0.266) among the three groups of women.\u003c/p\u003e \u003cp\u003eAmong the 1recruited patients with HR-HPV infection, 9 were classified as monotype infections (59.6%), where the most frequent types were 16 (28 cases, 17.9%), 52 (12 cases, 7.7%), 18 (10 cases, 6.4%), and 58 (10 cases, 6.4%); 43 cases were double-type infections (27.6%); and 20 cases were multiple type of infections (12.8%). The pathological results of cervical biopsy under colposcopy showed that there were 78 cases of chronic cervicitis with squamous epithelial hyperplasia (NILM), 52 cases of LSIL, and 26 cases of HSIL. Statistics of routine indicators of leucorrhea in each group are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We speculate that the vaginal microecology of patients with HR-HPV infection increases and the cleanliness of the vaginal microenvironment decreases with increasing grade of cervical lesions. The imbalance in the vaginal flora may be related to the increase in the number of pathogenic anaerobes.(Cleanlines Degree I: A large number of vaginal epithelial cells and vaginobacteria were observed under the microscope. Degree II: Microscopically, vaginobacteria may be present, but there may be a few heterozygotes or pus cells. Degree III: Microscopically, there are a small number of bacteria, a large number of pus cells and heterobacteria. Degree IV: Colposis was not observed under the microscope, except for a few epithelial cells, mainly purulent cells and miscellaneous bacteria.)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of routine leucorrhea assessment in the different groups of patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCleanliness (n%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e positive\u003c/p\u003e \u003cp\u003e(n%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSialidase positive\u003c/p\u003e \u003cp\u003e(n%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLeukocyte esterase positive (n%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI-II\u0026deg;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIII\u0026deg;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIV\u0026deg;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNILM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (60.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69 (88.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11 (21.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50 (89.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (34.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9 (34.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25 (96.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9 (34.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e2. Analysis of the bacterial community and principal component analysis of the samples\u003c/p\u003e \u003cp\u003eWe analyzed 1 samples for the bacterial composition in the vagina. At the genus level, a total of 200 microorganisms were common to the three groups. There were 149 species of unique to the NILM group, 86 to the LSIL group, and 73 to the HSIL group.As the degree of cervical lesions increased, the microbial diversity in the sample decreased slightly and then increased significantly. In the three groups of samples, we take the mean value of the species of the colony for comparison \u003cem\u003eLactobacillus\u003c/em\u003e was the most abundant, followed by \u003cem\u003eGardnerella\u003c/em\u003e. As the degree of cervical lesions increased, the abundance of \u003cem\u003eAtopobium\u003c/em\u003e, \u003cem\u003eSneathia\u003c/em\u003e, and other bacteria increased, and the abundance of \u003cem\u003eStreptococcus\u003c/em\u003e decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).The Alpha diversity of the three groups was compared by dilution curve. The results showed that the dilution curve of vaginal flora in HSIL group was obviously steep compared with that in the other two groups, and the dilution curve in LSIL group was slightly gentle than that in NILM group. Vaginal flora diversity may decrease first and then increase significantly. sobs index was used to compare the Alpha diversity of vaginal flora in the three groups. the results showed that there were significant differences between HSIL group and NILM group and LSIL group (Wilcoxon rank-sum test,P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).Comparing the beta diversity of three groups of samples through PCoA principal component analysis, the results showed that based on binary_lennon distance, HSIL group and the other two groups could be clearly distinguished (ANOSIM test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but there was no significant difference between NILM group and LSIL group (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3. Analysis of sample metabolites\u003c/p\u003e \u003cp\u003eA total of 164 known metabolites were detected in the GC-MS spectra of 1 samples, and more than ten metabolites were found to be different between two groups, low abundances of several metabolites compared to NILM and HSIL(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Through the analysis of the peak values obtained from each group of metabolites, the metabolites that were different between the NILM group and the LSIL group in descending order were taurine, guanine, uric acid, xanthine, β-mannosylglycerate, tyramine, tyrosine, phenylacetaldehyde, adenine, purine, and so on. The metabolites that were different between the LSIL and HSIL groups in descending order were sucrose, sarcosine, pyrogallol, cetyl alcohol, γ-aminobutyric acid, stearyl alcohol, dioctyl phthalate, ketoisocaproic acid, uric acid, guanine, and taurine. The metabolites that were different metabolites between the LSIL and HSIL groups, in descending order, were phenylacetaldehyde, adenosine, α-aminoadipate, glucalditol, asparagine, cresol, palmitic acid, lyxono-1, 4-lactone, palmitoleic acid, hydroxyurea, talose, etc.\u003c/p\u003e \u003cp\u003eUsing the Mann-Whitney U test, peak of taurine was determined to be the metabolite that was the most significantly different between the NILM and LSIL groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and between the LSIL group and HSIL group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Peak of guanine production was significantly different between the NILM and LSIL groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 ), and between the LSIL and HSIL groups (P\u0026thinsp;=\u0026thinsp;0.0021). Peak of uric acid production was significantly different between the NILM and LSIL groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and between the LSIL and HSIL groups (P\u0026thinsp;=\u0026thinsp;0.0102) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese results show that as the degree of cervical lesions deepens, amino acids, purines, sugars, and esters in the cervicovaginal microenvironment are consumed in large quantities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOPLS-DA was performed to determine the changes in the cervicovaginal metabolic patterns of HPV-infected patients with different types of cervical lesions. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that the metabolic patterns in the HSIL patients were significantly different from those in the NILM and the LSIL patients. The OPLS-DA scores for the NILM and HSIL groups were R2Y\u0026thinsp;=\u0026thinsp;0.765, Q2\u0026thinsp;=\u0026thinsp;0.407; and the OPLS-DA scores for the LSIL and HSIL groups were R2Y\u0026thinsp;=\u0026thinsp;0.921, Q2\u0026thinsp;=\u0026thinsp;0.594.It is not observed that there is a tendency of separation of metabolic patterns between NILM and LSIL (R2Y\u0026thinsp;\u0026lt;\u0026thinsp;0.5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e4. Influence of the vaginal flora on patients with HR-HPV infection\u003c/p\u003e \u003cp\u003eAfter using MetaboAnalyst to analyze the metabolic pathways of the aforementioned potential markers, pathway topology analysis was performed to determine the relevant pathways with an impact value\u0026thinsp;\u0026gt;\u0026thinsp;0.1. Four metabolic pathways correlated with LSIL, while three metabolic pathways correlated with HSIL (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e5. Correlation between the vaginal flora and metabolites\u003c/p\u003e \u003cp\u003eThe metabolites were screened by variance inflation factor (VIF) analysis, and clinical factors with small multicollinearity were retained for follow-up analysis. Factors with a VIF\u0026thinsp;\u0026gt;\u0026thinsp;10 are generally considered useless. Therefore, metabolites with a VIF\u0026thinsp;\u0026gt;\u0026thinsp;10 were filtered out first, and then the correlation coefficient between the metabolites and the top 50 abundant colony species were calculated through the correlation heatmap analysis. A variety of metabolites were significantly associated with the vaginal flora (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Taurine positively correlated with \u003cem\u003eWeissella\u003c/em\u003e, \u003cem\u003ePorphyromonas\u003c/em\u003e, and the \u003cem\u003eRikenellaceae\u003c/em\u003e RC9 gut group, and negatively correlated with \u003cem\u003eCorynebacterium\u003c/em\u003e, \u003cem\u003eRhodococcus\u003c/em\u003e, and \u003cem\u003eRalstonia solanacearum\u003c/em\u003e. Hydroxyl products such as hydroxylamine, hydroxyurea, hydroxybutyric acid, and hydroxyphenyl propionic acid were significantly positively correlated with the various vaginal flora, but negatively correlated with \u003cem\u003eLactobacillus\u003c/em\u003e. Meanwhile, annitol, glycerol, guanine, adenine, sarcosine, tyrosine, threonine, etc. positively correlated with \u003cem\u003eLactobacillus\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we found that cervicovaginal flora is closely associated with cervical cancer patients with high blood pressure and persistent HR-HPV infection. The vaginal flora of most HPV-infected women was still primarily composed of \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eGardnerella\u003c/em\u003e. However, with the increase of cervical lesion grade, the Alpha diversity of vaginal flora may decrease first and then increase significantly. The biological diversity of vaginal microecology in HSIL patients is significantly different from that in LSIL patients. The abundance of \u003cem\u003eAtopobium\u003c/em\u003e and \u003cem\u003eSneathia\u003c/em\u003e increased and that of \u003cem\u003eStreptococcus\u003c/em\u003e decreased with increasing grade of cervical lesions. This change in microbial abundance may affect the metabolic capacity of the microenvironment of the cervix and vagina. A healthy cervicovaginal microenvironment is primarily composed of slow-proliferating or quiescent cells. Changes in the cervicovaginal microbial composition caused by HPV infection may alter cellular metabolism to promote abnormal cell proliferation and protein synthesis.\u003c/p\u003e \u003cp\u003eWe detected a total of 164 known metabolites and found that taurine levels changed significantly in women at risk of cervical cancer. Other metabolites whose levels also significantly changed were mainly amino acids, purines, and sugars. Further pathway analysis also found 7 metabolic pathways that may be related to it, including aminoacyl-tRNA biosynthesis pathway, phenylalanine and tyrosine and tryptophan pathways, alanine and aspartic acid and glutamine Acid metabolism pathway, d-glutamine and d-glutamate metabolism pathway, arginine biosynthesis pathway, β-alanine metabolism pathway, phenylalanine metabolism pathway.A variety of metabolites were significantly associated with the vaginal flora. Taurine positively correlated with \u003cem\u003eWeissella\u003c/em\u003e, \u003cem\u003ePorphyromonas\u003c/em\u003e, and other miscellaneous bacteria, and negatively correlated with \u003cem\u003eCorynebacterium\u003c/em\u003e, \u003cem\u003eRhodococcus\u003c/em\u003e, and \u003cem\u003eRalstonia solanacearum\u003c/em\u003e. \u003cem\u003eLactobacillus\u003c/em\u003e is positively correlated with purines and amino acids, and negatively correlated with hydroxyl products. The massive consumption of key amino acid metabolites involved in cell growth, such as taurine, indicates that only HPV-positive and SIL patients\u0026rsquo; healthy cell metabolism is disrupted, which is a potential functional consequence of HPV-induced cervical-vaginal dystrophy [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTaurine is a rare amino acid and the most abundant free amino acid in the heart, retina, skeletal muscle, and white blood cells. It plays a vital role in energy metabolism [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and has also been shown to protect tissues in several oxidative-damage animal models. It can be converted into taurine chloramine, which is more stable and less toxic. Taurine chloramine acts a potent regulator of the mouse and human immune system by downregulating the production of pro-inflammatory mediators in leukocytes. It can also inhibit the activation of NF-κB, an effective signal transduction factor for inflammatory cytokines [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The latest research shows that in the process of exploring the body's natural defenses against bacterial infections, it was found that low levels of taurine can cause pathogenic bacteria to colonize the intestines, and high levels of taurine can produce enough hydrogen sulfide to prevent disease. Taurine can be prepared to prepare the intestinal flora to prevent infection. In this study, taurine was significantly reduced in LSIL and significantly increased in HSIL, indicating that taurine is related to the defensive effect of cervicovaginal flora [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Recent research on the body's natural defenses against bacterial infections has shown that low levels of taurine can cause pathogenic bacteria to colonize the intestines, and high levels of taurine can lead to production of enough hydrogen sulfide to prevent disease. In this study, taurine level was significantly reduced in LSIL and significantly increased in HSIL, indicating that it is related to the defense function of the cervicovaginal flora [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Guanine has been shown to regulate the enteric nervous system [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and the oxidative damage caused by 7,8-dihydro-8-oxoguanine, a derivative of guanine, is also significantly related to inflammation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Uric acid is a heterocyclic purine derivative and the final oxidation product of purine metabolism. Ryu et al. reported that uric acid can reduce the expression of E-cadherin in rat renal tubular epithelial cells, thereby disrupting the ability of epithelial cells to regulate factors such as nitric oxide that are needed to increase renal blood flow [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In addition, uric acid crystals can activate innate host defense mechanisms by changing membrane lipid composition, triggering inflammation, and activating immune responses [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].Metabolic abnormalities such as amino acids, purines, and uric acid prove that HR-HPV patients have enhanced inflammatory response and suppressed immune defense response. Compared with the NILM group, uric acid was down-regulated in the HSIL group, and there was no statistical difference between taurine and guanine between the two groups. Compared with the NILM group, taurine, guanine, and uric acid were significantly down-regulated in the LSIL group; compared with LSIL in the HSIL group, they were significantly up-regulated, which proves that the initial progression of cervical lesions will consume a large amount of amino acids, purines, etc. in the cervix-vaginal environment. As the disease level increases, amino acids, purines and other metabolites may produce compensatory up-regulation and participate in the immune defense function of cervical and vaginal epithelial cells.\u003c/p\u003e \u003cp\u003eTaken together, the cervicovaginal flora may play a major role in activating inflammation and the immune response by regulating the synthesis and decomposition of amino acids, esters, purines, and lipids. Using the multi-omics combination of analyzing the microbiome and its metabolome, we can infer the types of bacteria that change the characteristic metabolites that leads to cervical vaginal inflammation and progression of cervical precancerous lesions. Based on this information, we can consider the risk of cancer progression by exploring changes in these metabolites. The method we presented here provides a new diagnostic strategy for preventing and preventing precancerous lesions in patients with HPV infection. For the first time, taurine was found in vaginal lavage fluid samples in this study and can be a potential biomarker for cervical cancer. However, this pilot study did not consider interfering factors such as age, BMI, STIs, vaginal disorders (BV and AV), regional and personal lifestyle differences; thus, a larger sample size is needed for further verification of the results as well as a higher number of cervical cancer patients and stricter inclusion criteria for analysis and comparison. If verified in larger studies, the metabolites that have been correlated with cervical precancerous lesions and cervical cancer (taurine, guanine, and uric acid) in this study may be used as one of clinical diagnostic markers in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental study comply with relevant privacy protection laws and guidelines. This study has obtained ethical approval from Ethics Committee of Affiliated Hospital of Nanjing University of Chinese Medicine with approval number 2021NL-025-03.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eauthor\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSS: Conceptualization, Investigation, Writing - Original Draft, Visualization\u003c/p\u003e\n\u003cp\u003eSZ: Data Curation, Investigation\u003c/p\u003e\n\u003cp\u003eJS: Validation, Supervision\u003c/p\u003e\n\u003cp\u003eQR: Writing - Review \u0026amp; Editing, Project administration\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003edata availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e16s gene sequencing data uploaded to NCBI database, BioProject: PRJNA722355\u003c/p\u003e\n\u003cp\u003eMetabolomics data that supports the findings of this study are available in the supplementary material of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors disclosed no relevant realtionships.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWei Lihui, ZHAO Yun, SHEN Danhua, et al. 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EBioMedicine. 2019;44:675\u0026ndash;690.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"HR-HPV, SIL, Cervicovaginal microecology, 16S rRNA sequencing, metabolite","lastPublishedDoi":"10.21203/rs.3.rs-3066333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3066333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh-risk human papillomavirus (HR-HPV) causes cervical squamous intraepithelial lesions and cervical cancer; however, only a small group of people infected with HR-HPV will develop cervical precancerous lesions or cervical cancer. Current studies have shown that an imbalance in the cervicovaginal flora may be one of the factors for persistent HR-HPV infection. Cervicovaginal secretions are easily accessible and may be advantageous tools for diagnosing risks for cervical cancer. Thus, in this pilot study we collected 156 cervicovaginal secretions of women with HPV infection with precancerous cervical lesions to determine whether microflora-derived metabolites present in the secretion can be used for assessing the risk of cervical cancer in patients. We performed 16S rRNA sequencing and metabolomic analyses to identify changes in the cervicovaginal flora and its metabolites in patients with HPV infection with different grades of cervical lesions. We detected 164 common known metabolites in the three groups of samples. There are significant differences in the metabolic patterns of cervical lesion groups with different degrees, and multiple metabolites are significantly related to the vaginal flora. Among them, the content of metabolites such as taurine, guanine, and uric acid are significantly different in patients with persistent HR-HPV infection with different degrees of cervical lesions. Taurine is positively correlated with Weissella, and negatively correlated with Corynebacterium. Related. Which may involve 7 related metabolic pathways. Metabolites such as taurine, guanine, and uric acid are closely related to the activation of the human immune system. Cervical vaginal flora can regulate the synthesis and decomposition of amino acids, purines, and uric acid, and may play a major role in activating inflammation and the immune system. These metabolisms It is worthy of further research to assess the risk of HR-HPV infection progressing to cervical cancer.\u003c/p\u003e","manuscriptTitle":"Correlations between microbiota-derived metabolites and cervical precancerous lesions in women with HPV","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-11 14:10:29","doi":"10.21203/rs.3.rs-3066333/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":"11ec0682-9955-44eb-ba01-d3396618fb82","owner":[],"postedDate":"July 11th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":23001211,"name":"Biological sciences/Microbiology/Clinical microbiology"},{"id":23001212,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":23001213,"name":"Health sciences/Diseases/Urogenital diseases"}],"tags":[],"updatedAt":"2023-08-22T03:44:20+00:00","versionOfRecord":[],"versionCreatedAt":"2023-07-11 14:10:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3066333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3066333","identity":"rs-3066333","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}