Metabolomic and Microbiota Profiles in Cervicovaginal Lavage Fluid of women with High- Risk Human Papillomavirus Infection

doi:10.21203/rs.3.rs-4964585/v1

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Metabolomic and Microbiota Profiles in Cervicovaginal Lavage Fluid of women with High- Risk Human Papillomavirus Infection

https://doi.org/10.21203/rs.3.rs-4964585/v1

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The presence of high-risk human papillomavirus (HR-HPV) contributes to the development of cervical lesions and cervical cancer. Recent studies suggest that an imbalance in the cervicovaginal microbiota might be a factor in the persistence of HR-HPV infections. In this study, we collected 156 cervicovaginal fluid(CVF) of women with HR-HPV infection, which were divided into three groups(negative for intraepithelial lesions = 78, low/high-grade squamous intraepithelial lesions = 52/26). We performed metabolomics and 16S rRNA sequencing to identify changes in metabolites and cervicovaginal microbiota among patients with HR-HPV infection and varying grades of cervical lesions. We detected 164 metabolites and 389 flora types in the three groups. Ten CVF metabolites—N-methylalanine, phenylacetaldehyde, succinic acid, 2-3-dihydroxypyridine, DL-p-hydroxylphenyllactic acid, gluconic acid lactone, guanine, glucose-6-phosphate, erythrose, and sucrose showed significant associations with disease severity (P < 0.05) and distinct separation patterns in HR-HPV-infected patients with LSIL and HSIL, with an area under the curve of 0.928. The most abundant microbial communities in the CVF were Lactobacillus and Gardnerella. There was a significant negative correlation between succinic acid and Gardnerella. These findings suggest significant systemic metabolic changes in HR-HPV infection as it progresses to cervical lesions, providing valuable insights into the associated metabolic alterations and their association with disease severity.

metabolomics

16S rRNA sequencing

human papillomavirus

cervicovaginal fluid

High-risk human papillomavirus (HR-HPV) infection was identified in nearly 90% of cervical squamous intraepithelial lesions (SIL) and 99% of cervical cancer tissue samples, as indicated by a 15-year, population-based cohort study conducted in China¹. Cervical SILs are prevalent conditions that pose a significant threat to women's health and are closely associated with invasive cervical cancer. This malignancy is characterized by a rising incidence rate and an emerging trend towards earlier onset^2–3, which poses a threat to women's health and overall quality of life. Current research indicates that disruptions in the vaginal microbiota are associated with bacterial vaginosis and various infectious disorders. The progression of HR-HPV infection is influenced by complex interactions between host immune responses and viral factors, as well as by the delicate balance of the vaginal microbiome⁴. Several studies have shown that disturbances in the cervicovaginal microbiota can result in persistent HR-HPV infections and may contribute to the development of SIL^5–6. However, the underlying mechanisms remain unclear and require further investigation.

Metabolomics, with its capability to detect thousands of small molecules within an ecosystem, has significantly advanced the exploration of metabolites. Paired microbiome-metabolome studies have provided valuable mechanistic insights into host-microbiome interactions across a variety of pathologies7. Specifically, the vaginal metabolome of women with HPV infection exhibits distinct profiles compared to that of HPV-negative counterparts, with notable differences encompassing biogenic amines, glutathione, glycogen, and lipid metabolism-related metabolites⁸. Compared to HPV-negative individuals, the HPV-positive group demonstrates a reduction in metabolite numbers, while the cervical cancer group shows an increase. This non-linear trend suggests that HPV infection and SILs deplete metabolites in certain amino acid and nucleotide pathways, whereas cervical cancer results in metabolite enrichment from diverse lipid pathways⁹. HPV infection can activate various metabolic pathways, directly affecting enzymes in the glycolytic pathway, enhancing glucose uptake and glycolysis, and promoting the pentose phosphate pathway, lactate dehydrogenase A synthesis, and β-oxidation inhibition to facilitate the Warburg effect¹⁰.

Academic reports indicate that HPV infection influences mucosal metabolism and host immune status, and conversely, vaginal microbiota and host immunity can impact HPV infection^11–13 HPV-infected individuals harbor a highly diverse cervicovaginal microbiome¹⁴. Chronic HPV infection has been associated with imbalances in the vaginal microecology and damage to the cervicovaginal mucosal barrier¹⁵. Prolonged inflammation within the cervix and vagina may contribute to cervical epithelial degeneration and necrosis, compromising the cervical epithelial barrier and triggering an immune response. This vulnerability can facilitate the propagation of other pathogenic organisms, perpetuating HPV infection and potentially leading to the development of SILs ¹⁶.

Emerging research reveals that metabolites produced by distinct bacterial groups may play roles in disease etiology or protection. Cervical microbial metabolites may enter the systemic circulation, influencing the overall health of high-risk cervical cancer patients¹⁷. Consequently, metabolites derived from or altered by the microbiome have garnered significant attention, highlighting their potential to impact the host locally and systemically^16–19.

Taken together, the current studies reveal that a disturbed vaginal microbiota not only impairs immune responses but also facilitates the maintenance of HR-HPV infection and the progression of SIL. The alterations in metabolites attributed to the vaginal flora may disclose the risks associated with cervical cancer among patients infected with HR-HPV ²⁰. Cervical lavage fluid originates from the secretions within the cervicovaginal cavity. The procedure for collecting cervical lavage fluid is more straightforward and less invasive when contrasted with the collection of cervical exfoliated cells. Consequently, it may serve as a viable tool for monitoring biomarkers to evaluate the risks of cervical cancer among women with HR-HPV infection. As a result, in the present study, we collected cervicovaginal lavage fluid samples from women infected with HPV who displayed various degrees of cervical lesions. We characterized the corresponding changes in the microbial flora and its associated metabolites in search of potential biomarkers capable of predicting the risk of SIL progression. The findings presented here offer valuable insights into diagnostic and monitoring strategies designed to prevent the advancement of SIL in women with HR-HPV infection.

Baseline characteristics. The clinical baseline data were presented in Table 1, which included age in years, pregnancy times, and childbirth times, all presented as the mean ± standard deviation. The table revealed no significant differences in age (P = 0.293), pregnancy times (P = 0.452), and childbirth times (P = 0.266) among the three groups of women.

Table 1

Basic information of the participants
	NILM (N = 78)	LSIL (N = 52)	HSIL (N = 26)	P-Value
Age(years)	35.60 ± 7.843	37.50 ± 9.648	37.91 ± 3.22	0.293
gestation(times)	2.40 ± 1.761	2.04 ± 1.414	2.27 ± 1.343	0.452
production(times)	1.12 ± 0.581	0.94 ± 0.639	1.12 ± 0.711	0.266
Leucorrhea cleanliness(n%)
I-II°	47(60.3%)	25(48.1%)	8(30.8%)	-
Ⅲ°	26(33.3%)	16(30.8%)	9(34.6%)	-
IV°	5(6.4%)	11(21.2%)	9(34.6%)	-
H₂O₂ positive(n%)	69(88.5%)	50(89.3%)	25(96.2%)	-
Sialidase positive(n%)	19(24.4%)	14(8.7%)	9(34.6%)	-
Leukocyte esterase positive(n%)	18(23.1%)	10(17.9%)	7(26.9%)	-
note: There was no statistically significant difference in age (F = 1.237, P = 0.293), gestation (F = 0.798, P = 0.452), and production (F = 1.336, P = 0.266) among the three groups of women (P > 0.05). Leucorrhea cleanliness values of Ⅲ° and IV° degrees indicate poor cleanliness. According to the chi square test, the p-value is 0.029 (p < 0.05).

Among the patients with HR-HPV infection under study, 93 (59.6%) exhibited monotype infections. The most common type was HPV 16, affecting 28 cases (17.9%), followed by HPV 52 with 12 cases (7.7%), HPV 18 with 10 cases (6.4%), and HPV 58 with 10 cases (6.4%). There were 43 cases (27.6%) with double-type infections and 20 cases (12.8%) with multiple infections. Clinical information regarding leucorrhea for each group is presented in Table 1. A chi-square test conducted on the vaginal microbiota of the three groups indicated statistically significant differences in the cleanliness of leucorrhea (P = 0.029). The progression of cervical lesions in HR-HPV-infected patients was associated with a deteriorating condition, which was further correlated with an increase in positive results for hydrogen peroxide, sialidase, and leukocyte esterase.

Analysis of CVF sample metabolites. A total of 164 metabolites were identified in the CVF samples. The total ion chromatograms (TICs) of the CVF samples, which were obtained using GC-MS, were shown in Fig. 1. The relative standard deviation (RSD) of the internal standard was less than 12%, while the RSD of the quality control (QC) sample was less than 17.2%. The quality accuracy was controlled within 10 ppm, and the variation in retention time was less than 0.1 minute. OPLS-DA models clearly separated the CVF samples into two groups: NILM and SIL (LSIL + HSIL) groups, as well as the LSIL and HSIL groups. The good fit and predictive capabilities of the OPLS-DA model were estimated using R2Y, where the first value represented the fit and the second value represented the predictive capability. A permutation P-value less than 0.01 confirmed the model's reliability and indicated the absence of overfitting, as shown in Figs. 2B and D. Figure 2 indicated that the metabolic patterns in HSIL patients were significantly different from those in NILM and LSIL patients.

Differential metabolites and pathway analysis in cervical lesions. A total of 108 differential metabolites were identified between the NILM group and the SIL group (LSIL + HSIL), with the highest-ranking metabolites including O-phosphothreonine, N-methylglutamic acid, tryptophan, melezitose, glutamine, gamma-aminobutyric acid, alpha-aminoadipic acid, sedoheptulose, N-methylalanine, and spermidine. Detailed information was provided in Table S1.

A total of 31 differential metabolites were identified between the LSIL group and the HSIL group, with the highest-ranking metabolites including sucrose, erythrose, iminodiacetic acid, glucose-6-phosphate, pyrogallol, 2-3-dihydroxypyridine, N-methylalanine, tyrosine minor and erythronic acid lactone. Data was presented in Table S2.

The heatmap results showed that most differential metabolites with low abundances were found in LSIL compared to NILM and HSIL (Fig. 3A). There are 10 different metabolites among the NILM and SIL groups, and the LSIL and HSIL groups (Fig. 3B). After using MetaboAnalyst to analyze the metabolic pathways of the aforementioned 10 potential markers, pathway topology analysis was performed to determine the relevant pathways with an impact value > 0.1. Further pathway analysis also found 5 metabolic pathways that may be related to them, including: (A) starch and sucrose metabolism, (B) phenylalanine metabolism, (C) neomycin, kanamycin, and gentamicin biosynthesis, (D) butanoate metabolism, and (E) TCA cycle (Fig. 3C).

Diagnostic performance of a 10-metabolite panel in cervical lesions. These 10 differential metabolites show significant differences between the LSIL group and the HSIL group (Fig. 4), including the following metabolites: 2,3-dihydroxypyridine, DL-p-hydroxylphenyllactic acid, erythrose, glucose-6-phosphate, gluconic acid lactone, guanine, N-methylalanine, and phenylacetaldehyde, which were increased in the HSIL group, while succinic acid and sucrose were decreased in the HSIL group. Additionally, they show significant differences between the NILM group and the SIL group (Figure S1).

The performance of the 10-metabolite panel for HR-HPV-infected cervical lesions was assessed using the area under the curve (AUC) calculated by the random forest algorithm. The metabolite panel in CVF demonstrated good accuracy in distinguishing between the NILM group and the SIL group (LSIL + HSIL), with an AUC of 0.811 (Fig. 5A), and between the LSIL group and the HSIL group, with an AUC of 0.928 (Fig. 5B).

Phenylacetaldehyde is an aromatic fatty aldehyde primarily derived from the metabolic processes of fatty acid in animal bodies. It has been shown to inhibit cancer cell proliferation and promote apoptosis in these cells. Furthermore, phenylacetaldehyde reduces the expression of cancer stem cell (CSC) marker genes, including CD44+/CD24- and ALDH1. It also preferentially induces the production of reactive oxygen species (ROS), decreases the phosphorylation of nuclear Stat3, and lowers IL-6 secretion, thereby inhibiting the activation of the Stat3 signaling pathway. Because of these properties, phenylacetaldehyde is considered a potential therapeutic agent for cancer and cancer stem cells²¹. In our study, we observed that phenylacetaldehyde levels were increased in the HSIL group, suggesting a protective compensatory mechanism in cervical lesions.

Glucose-6-phosphate is a product formed by the phosphorylation of various sugars that are metabolized into glucose after entering cells²⁵. In our study, we observed an upregulation of glucose-6-phosphate and a downregulation of sucrose, indicating that glucose metabolism is enhanced in cervical lesions. This increased metabolism may contribute to viral replication and exacerbate the severity of the lesions^26–27.

Succinic acid is a metabolic intermediate of amino acids in the tricarboxylic acid (TCA) cycle within host cells. It can be metabolized by somatic cells, and current research indicates that succinic acid and its derivatives exhibit anticancer activity by inducing cell apoptosis^22–24. In our study, we found that succinic acid levels were decreased, which suggests that high-risk HPV infection leads to increased cellular proliferation. This proliferation may exacerbate disease progression and raise the potential for further advancement toward cervical cancer.

Analysis of the bacterial community in cervical lesions. We analyzed 156 CVF samples to assess the bacterial composition in the vagina. Alpha diversity was compared using dilution curves, showing a steeper curve in the HSIL group compared to the others, while the LSIL group had a gentler curve than the NILM group, indicating that vaginal microbiota diversity may initially decrease before significantly increasing (Fig. 6A). Using the Sobs index, we found significant differences in alpha diversity between the HSIL group and the NILM and LSIL groups (P < 0.05). Microbial diversity slightly decreased from NILM to LSIL (P > 0.05) but significantly increased from LSIL to HSIL (P = 0.0042) and from NILM to HSIL (P = 0.0182) (Fig. 6B). Beta diversity analysis via principal coordinate analysis (PCoA) showed distinct separation of the HSIL group from the others based on binary Jaccard distance (ANOSIM test, P < 0.05), with no significant difference between the NILM and LSIL groups (P > 0.05) (Fig. 6C, D).

At the genus level, we identified 152 microorganisms that were common across the three groups. Lactobacillus was the most abundant genus, followed by Gardnerella. As the severity of cervical lesions progressed, the abundance of Atopobium and Sneathia increased, while Streptococcus decreased (Fig. 7A, C). The NILM group exhibited 149 unique species, the LSIL group had 86, and the HSIL group had 73 unique species (Fig. 7B). The Vaginal Microbial Health Index (VMHI) serves as a robust measure for evaluating health status based on species-level classification features of vaginal microbiome samples. Our analysis revealed that the HSIL group had significantly lower VMHI scores compared to the LSIL group, and there was also a notable decrease in VMHI scores in the SIL group compared to the NILM group (Figure S2).

This study found a close association between cervicovaginal microbiota and cervical cancer in patients with persistent HR-HPV infection. The cervicovaginal environment is home to various bacteria, with Lactobacillus species frequently recognized as biomarkers of health²⁸. These bacteria play a crucial role in maintaining a balanced micro-ecosystem, which can be disrupted by changes in the host, leading to inflammation and viral infections. HPV infection and clearance rates are correlated with the presence of Langerhans cells and a microbiome dominated by Lactobacillus geeseri^29,13. Additionally, Lactobacillus enhances the immune response by stimulating phagocytes and promoting the production of cytokines, including IL-10, IL-12, IFN-γ, and TNF-α³⁰. A 16-week cohort study revealed that HPV-positive women had a vaginal microbiota predominantly composed of Lactobacillus iners and anaerobic bacteria¹³. Notably, Lactobacillus indolent may hinder HPV clearance, as women with this species experience lower clearance rates compared to those with Lactobacillus crispatus²⁰.

A study has shown that in women lacking Lactobacillus crispatus, the competitive proliferation of three bacterial groups—Lactobacillus iners, Gardnerella vaginalis, and Anaerococcus vaginalis—can lead to the development of squamous intraepithelial lesions (SIL)³¹. Additionally, some research indicates that Lactobacillus may have a plasmid-like function that allows it to integrate the HPV16 gene, thereby preventing the virus from integrating with host cells³². The loss of Lactobacillus dominance promotes the colonization of anaerobic bacterial species, increasing microbial diversity. This shift often results in changes to immunity and epithelial homeostasis through various mechanisms, which can, in turn, facilitate HPV infection¹⁸.

While the vaginal microbiota of most HPV-infected women primarily consists of Lactobacillus and Gardnerella, the alpha diversity of vaginal microbiota tends to decrease initially with increasing cervical lesion grade before significantly rising. The biological diversity of vaginal microecology in patients with high-grade squamous intraepithelial lesions (HSIL) is markedly different from that in patients with low-grade squamous intraepithelial lesions (LSIL). As the grade of cervical lesions increases, the abundance of Atopobium and Sneathia rises, while Streptococcus abundance decreases. These changes in microbial composition may affect the metabolic capacity of the cervical and vaginal microenvironment.

Correlation between vaginal microbiota and metabolites. We analyzed the correlation between 10 differentially expressed metabolites and the top 50 bacterial populations based on abundance. Metabolites were screened using variance inflation factor (VIF) analysis, excluding those with a VIF greater than 10 due to weak correlations. The correlation coefficients between the selected metabolites and the top 50 abundant bacterial species were computed through heatmap analysis, revealing significant associations (Fig. 8). Sucrose positively correlated with Flavobacterium, a Gram-negative bacterium that can produce acid from sugar, potentially causing inflammation when the immune system is weak³³. Phenylacetaldehyde was positively correlated with Pseudomonas, where phenylacetaldehyde dehydrogenase converts it to phenylacetic acid in the detoxification pathway³⁴. Succinic acid showed positive correlations with Klebsiella, DNF00809, and Sneathia, but negative correlations with Gardnerella and Veillonella. Gardnerella, a key pathogen in bacterial vaginosis (BV), increases succinic acid concentrations, contributing to inflammation³⁵. Glucose-6-phosphate positively correlated with Shuttleworthia and Dialist, while negatively correlating with Aerococcus and Peptoniphilus. The consumption of key amino acid metabolites indicates disrupted cell metabolism in HPV-positive and SIL patients, suggesting potential HPV-induced cervical-vaginal malnutrition³⁶. This study observed an increase in the expression of Lactobacillus-related metabolites, such as DL-p-hydroxyphenyllactic acid, as well as sugar metabolism-related enzymes like erythritol and glucose-6-phosphate in SIL. This indicates that glycolysis is already enhanced at the SIL stage, potentially contributing to the development of cervical cancer. Additionally, increased carbohydrate consumption in cervical cancer tissue can lead to elevated lactate production. When oxygen levels are sufficient, cells tend to generate energy through glycolysis rather than oxidative phosphorylation. This mechanism allows cancer cells to rapidly produce adenosine triphosphate (ATP), providing a quick energy source. Furthermore, the intermediate products of glycolysis support the material synthesis needs of rapidly proliferating tumor cells²⁶.

In summary, this study clarifies the pivotal roles of metabolic dysregulation and microbiome alterations in the escalation of HR-HPV infection to cervical lesions. The analysis of cervicovaginal fluids from women with HR-HPV infections uncovered a distinctive metabolic profile, characterized by ten distinct metabolites. These metabolites are significantly associated with lesion severity and can effectively differentiate between LSIL and HSIL. The prevalence of Lactobacillus and Gardnerella in the cervicovaginal microbiota, along with a negative correlation between succinic acid levels and Gardnerella, indicates an intricate interaction between microbial composition and metabolic processes. These findings offer new insights into the metabolic and microbial landscape of HR-HPV infections, emphasizing the need for further research to investigate the clinical implications of these metabolic and microbial changes in cervical disease.

Subjects of the study. Between May and July 2020, a total of 156 women were recruited from the Gynecology Department of Jiangsu Hospital of Chinese Medicine, who were persistently infected with HR-HPV genotypes. Laboratory results confirmed HR-HPV infection through the detection of specific genotypes, HPV E6/E7 mRNA, or HPV 23 typing. Persistent HR-HPV infection was defined as the detection of two or more consecutive positive tests, with each test occurring at least six months apart. Women with atypical squamous cells of undetermined significance (ASCUS) or more significant cervical abnormalities, detected via thinPrep cytology test (TCT), were referred for colposcopy and cervical biopsy to confirm the presence of lesions and to enable further histopathological analysis for definitive diagnosis.

The participants were divided into three cervical biopsy-based groups: women with Negative for Intraepithelial Lesions (NILM, n = 78), those with Low-Grade Squamous Intraepithelial Lesions (LSIL, n = 52), and those with High-Grade Squamous Intraepithelial Lesions (HSIL, n = 26). Inclusion criteria were as follows: non-menopausal women aged 30 years and older with regular menstrual cycles, and without a history of vulvovaginal candidiasis, sexually transmitted infections (e.g., chlamydia), or hormonal contraception use, along with the availability of comprehensive medical records. Exclusion criteria included a history of total hysterectomy, autoimmune disorders, immunosuppression, systemic diseases, recent treatment for cervical lesions, pregnancy, recent use of vaginal products, and sexual activity within 48 hours prior to sampling, with further details provided in the supplementary material. Ethical approval was obtained from the Ethics Committee of the Affiliated Hospital of Nanjing University of Chinese Medicine (approval number 2021NL-025-03). All experiments were carried out in accordance with the relevant guidelines and regulations, and informed consent was obtained from each participant.

Collection of cervicovaginal fluid(CVF). A sterile cotton swab was employed to collect secretions from the cervix and the superior one-third of the vaginal side wall of patients positioned in the lithotomy position. The swabs were subsequently transferred to a sterile test tube for routine leucorrhea detection, with the process initiated within 15 minutes of collection. The cervix and the superior one-third of the vaginal wall were rinsed with 5 mL of 0.9% sodium chloride solution. Following this, 3–4 mL of CVF was aspirated, homogenized, and 2 mL of the supernatant CVF was isolated. All CVF samples were immediately preserved at -80°C for future investigation. The preserved samples were then centrifuged at 4°C and 18,000 rpm for 10 minutes, resulting in the separation of the supernatant from the precipitate.

Chemicals and reagents. For metabolomics analysis, 1,2-13C myristic acid, which contains 1% BSTFA (trimethylchlorosilane), the C8-C40 alkane series, pyridine, and methoxyamine, were acquired from Sigma-Aldrich (USA). For 16S rRNA sequencing, the following reagents and kits were used: a DNA extraction kit from Omega Bio-Tek (Norcross, GA, USA), biowest agarose from biowest (ESP), FastPfu Polymerase from TransGen (China), the AxyPrep DNA Gel Extraction Kit from Axygen Biosciences (Union City, CA, USA), the NEXTFLEX Rapid DNA-Seq Kit from Bioo Scientific (USA), and the MiSeq Reagent Kit v3 from Illumina (USA).

Experimental instruments and equipment. A Trace 1310 gas chromatography system, coupled with a TSQ 8000 mass spectrometer (GC-MS, Thermo, Waltham, USA), is equipped with an AS 1310 automatic injector. It features a Tg-5ms gas chromatography column (0.25 mm × 30 m, 0.25 µm) suitable for GC-MS applications. Moreover, a Savant SPD1010 vacuum centrifugal concentrator (Thermo Fisher Scientific, USA) was used for sample drying. In addition, the following equipment is required for flora detection: a NanoDrop™ 2000 UV-visible spectrophotometer (Thermo Scientific, Wilmington, USA), an ABI GeneAmp® 9700 PCR thermocycler (ABI, CA, USA), a Quantus™ Fluorometer (Promega, USA), and a PE300 platform (Illumina, San Diego, USA).

16S rRNA sequencing. The centrifugation of CVF samples was conducted at 4°C and at 18,000 rpm for a period of 10 minutes. The precipitate was subsequently collected. Microbial community genomic DNA was extracted from the collected precipitate using the E.Z.N.A.® soil DNA kit, following the manufacturer’s instructions. The purity and concentration of the extracted DNA were assessed using a NanoDrop 2000 UV-vis spectrophotometer and confirmed on a 1% agarose gel. The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified using the primer pairs 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'), and the PCR amplification was carried out in a thermocycler. The PCR protocol included an initial denaturation step at 95°C for 3 minutes, followed by 27 cycles of denaturation at 95°C for 30 seconds, annealing at 55°C for 30 seconds, extension at 72°C for 45 seconds, a final extension phase at 72°C for 10 minutes, and a termination step at 4°C. The PCR mixture contained 4 µL of 5× TransStart FastPfu buffer, 2 µL of 2.5 mM dNTPs, 0.8 µL of each primer (5 µM), 0.4 µL of TransStart FastPfu DNA Polymerase, 10 ng of template DNA, and ddH2O to bring the final volume to 20 µL. Triplicate PCR reactions were performed.

The PCR products were purified from a 2% agarose gel using the AxyPrep DNA Gel Extraction Kit, and purified amplicons were pooled in equimolar amounts before being paired-end sequenced on an Illumina MiSeq PE300 platform, utilizing the standard protocols provided by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The raw sequencing reads were deposited in the NCBI Sequence Read Archive (SRA) under accession number SUB9475651.

Metabolomic analysis of cervicovaginal CVF samples. The CVF samples were centrifuged at 4°C at 18,000 rpm for 10 minutes to obtain the supernatant, which was then utilized for metabolomic analysis. The supernatant (500 µL) was concentrated to a dry pellet using a vacuum centrifugal concentrator for 2 hours. The dried supernatant from the CVF samples was reconstituted with 100 µL of sterile, distilled water. Subsequently, to each sample, 400 µL of an ice-cold methanol solution containing 1,2-13C myristic acid (12.5 µg/mL) was added. The solutions were vigorously vortexed for 3 minutes and centrifuged for 10 minutes at 4°C and 18,000 rpm. Thereafter, 200 µL of the supernatant was transferred to a centrifugal concentrator to evaporate for another 2 hours. Next, 30 µL of methoxyamine pyridine (10 mg/mL) was added to the samples. The samples were vortexed for 5 minutes and incubated at 30°C with gentle shaking at 450 rpm for 1.5 hours. Following this, 30 µL of N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) was introduced into the samples. The samples were vortexed again for 5 minutes and then allowed to incubate at 37°C with gentle shaking at 450 rpm for 0.5 hours. Finally, the samples were centrifuged for 10 minutes at 4°C and 18,000 rpm. The supernatant (50 µL) was collected for metabolite analysis. Concurrently, 3 µL of supernatant from each sample was taken and mixed in equal amounts to create quality control (QC) samples for methodological validation. After analyzing a sequence of 10 samples, a QC sample was inserted to assess the system's applicability and stability.

Statistical analysis

The clinical characteristics analysis was performed using PASW Statistics 23 (SPSS, Chicago, USA). Data preprocessing was conducted using Xcalibur 2.2. For the processes of raw peak extraction, peak alignment, deconvolution analysis, and identification, MS-DIAL software (Tsugawa et al., 2015) was employed, in conjunction with the Fiehn BinBase DB. Normalization was achieved via Probability Quotient Normalization (PQN). Subsequently, multivariate analyses were conducted including Principal Component Analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and pathway enrichment analysis, all executed using MetaboAnalyst 6.0 (https://www.metaboanalyst.ca/faces/home.xhtml). These analytical methods enabled the differentiation of metabolomic profiles within the CVF in HPV-positive female patients versus those who were HPV-positive and concurrently diagnosed with cervical lesions. Further statistical analyses were then performed using GraphPad Prism (version 8.0.2) and R Studio (version 3.4.1) software.

Comparisons of data between two groups were made through the two-sample t-test. For the screening of microbial profiles, Variance Inflation Factor (VIF) analysis was employed. The statistical significance of identified metabolites was determined using non-parametric analysis of variance (ANOVA) tests. A subsequent post-hoc analysis was performed using the Wilcoxon Mann-Whitney test. A significance threshold of p < 0.05 was established for all statistical assessments.

HR-HPV High-risk human papillomavirus

CVF Cervicovaginal fluid

NILM Negative for Intraepithelial Lesions

LSIL Low-grade squamous intraepithelial lesions

HSIL High-grade squamous intraepithelial lesions

TICs Total ion chromatograms

RSD Relative standard deviation

QC quality control

GC-MS Gas chromatography-mass spectrometry

AUC Area under the curve

CSC Cancer stem cell

ROS Reactive oxygen species

PCoA Principal coordinate analysis

VIF Variance inflation factor

ATP Adenosine triphosphate

Author contributions

Su Shen, Shixian Zhao contributed to metabolomic profiling analysis, data collection, and statistical analysis and interpretation. Su Shen, Jin-jun Shan and Qingling Ren contributed substantially to the study's concept and design, as well as the collection of samples and clinical data. Su Shen, Shixian Zhao contributed to sample collection and statistical analysis of data. Su Shen and Qingling Ren contributed to interpretation and drafting of the manuscript. All authors have read and approved the final manuscript.

Acknowledgement

This work was supported by the National Natural Science Foundation of China (No. 82074478).

Ethical statement

This study was conducted in accordance with the Declaration of Helsinki. The experimental procedures complied with relevant privacy protection laws and guidelines. Ethical approval was obtained from the Ethics Committee of the Affiliated Hospital of Nanjing University of Chinese Medicine (approval number 2021NL-025-03). All experiments were carried out in accordance with the relevant guidelines and regulations, and informed consent was obtained from each participant.

Data availability

Figures 6–8 and Supplementary Figure 2 are publicly available in the NCBI database under BioProject PRJNA722355 (www.ncbi.nlm.nih.gov/bioproject/PRJNA722355). Data supporting Table 1 is not publicly available to protect patient privacy. Figures 2–3 and Figure 5 were generated using the publicly available prognosis tool at www.metaboanalyst.ca/faces/home.xhtml. Figures 4 and Supplementary Figure 1 are included in the supplementary materials. Raw data supporting Figure 1 and all other raw data for metabolomics are available from the corresponding author upon reasonable request (email: [email protected]).

Conflict of Interest

The authors declare that they have no competing interests.

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.. Gynecologic Oncology, 2020, 156(3):616-623. doi:10.1016/j.ygyno.2019.12.027
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. JAMA Oncol,2017,3(4):524-548. doi:10.1001/jamaoncol.2016.5688
Martin. The application of precision medicine in the prevention and treatment of cervical cancer . Journal of Practical Obstetrics and Gynecology,2017,33(06):403-406. doi:1003-6946(2017)06-0404-04
Zhu Ruoxi, HAO Min, ZHAO Weihong, WANG Wei, WANG Zhlian, WANG Jintao, FENG Bo, Yang Jing, WANG Zhe, NIU Xiaofen. Relationship between changes in vaginal microbiological metabolites and the risk of high-risk human papillomavirus infection and cervical intraepithelial neoplasia . Chinese Journal of Practical Gynecology and Obstetrics, 2019, 35(07):797-802. doi:10.19538/j.fk2019070119
Champer M, Wong AM, Champer J, et al. The role of the vaginal microbiome in gynaecological cancer. BJOG. 2018;125(3):309-315. doi:10.1111/1471-0528.14631
Dahoud W, Michael CW, Gokozan H, Nakanishi AK, Harbhajanka A. Association of Bacterial Vaginosis and Human Papilloma Virus Infection With Cervical Squamous Intraepithelial Lesions. Am J Clin Pathol. 2019;152(2):185-189. doi:10.1093/ajcp/aqz021
Kindschuh WF, Baldini F, Liu MC, et al. Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome. Nat Microbiol. 2023;8(2):246-259. doi:10.1038/s41564-022-01293-8
Borgogna JC, Shardell MD, Santori EK, et al. The vaginal metabolome and microbiota of cervical HPV-positive and HPV-negative women: a cross-sectional analysis. BJOG. 2020;127(2):182-192. doi:10.1111/1471-0528.15981
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[J]. EBioMedicine. 2019;44:675-690.
Sitarz K, Czamara K, Szostek S, Kaczor A. The impact of HPV infection on human glycogen and lipid metabolism - a review. Biochim Biophys Acta Rev Cancer. 2022;1877(1):188646. doi:10.1016/j.bbcan.2021.188646
Scott M, Stites DP, Moscicki AB. Th1 cytokine patterns in cervical human papillomavirus infection. Clin Diagn Lab Immunol. 1999;6(5):751-755. doi:10.1128/CDLI.6.5.751-755.1999
Shannon B, Yi TJ, Perusini S, et al. Association of HPV infection and clearance with cervicovaginal immunology and the vaginal microbiota. Mucosal Immunol. 2017;10(5):1310-1319. doi:10.1038/mi.2016.129
Brotman RM, Shardell MD, Gajer P, et al. Interplay between the temporal dynamics of the vaginal microbiota and human papillomavirus detection. J Infect Dis. 2014;210(11):1723-1733. doi:10.1093/infdis/jiu330
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. Published 2020 Aug 26. doi:10.1186/s12879-020-05324-9
Champer M, Wong AM, Champer J, et al. The role of the vaginal microbiome in gynaecological cancer. BJOG. 2018;125(3):309-315. doi:10.1111/1471-0528.14631
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(1):9-18.e8. doi:10.1016/j.ajog.2018.12.011
Srinivasan S, Morgan MT, Fiedler TL, et al. Metabolic signatures of bacterial vaginosis. mBio. 2015;6(2):e00204-15. Published 2015 Apr 14. doi:10.1128/mBio.00204-15
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
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. doi:10.1038/srep16865
Łaniewski P, Barnes D, Goulder A, et al. Linking cervicovaginal immune signatures, HPV and microbiota composition in cervical carcinogenesis in non-Hispanic and Hispanic women. Sci Rep. 2018;8(1):7593. Published 2018 May 15. doi:10.1038/s41598-018-25879-7
Choi HS, Kim SL, Kim JH, Ko YC, Lee DS. Plant Volatile, Phenylacetaldehyde Targets Breast Cancer Stem Cell by Induction of ROS and Regulation of Stat3 Signal. Antioxidants (Basel). 2020;9(11):1119. Published 2020 Nov 13. doi:10.3390/antiox9111119
Connors J, Dawe N, Van Limbergen J. The Role of Succinate in the Regulation of Intestinal Inflammation. Nutrients. 2018;11(1):25. Published 2018 Dec 22. doi:10.3390/nu11010025
Iplik ES, Catmakas T, Cakmakoglu B. A new target for the treatment of endometrium cancer by succinic acid. Cell Mol Biol (Noisy-le-grand). 2018;64(1):60-63. Published 2018 Jan 31. doi:10.14715/cmb/2018.64.1.11
G, Ertugrul B, Iplik ES, Cakmakoglu B. The apoptotic efficacy of succinic acid on renal cancer cell lines. Med Oncol. 2021;38(12):144. Published 2021 Oct 23. doi:10.1007/s12032-021-01577-9
Park S, Dingemans J, Gowett M, Sauer K. Glucose-6-Phosphate Acts as an Extracellular Signal of SagS To Modulate Pseudomonas aeruginosa c-di-GMP Levels, Attachment, and Biofilm Formation. mSphere. 2021;6(1):e01231-20. Published 2021 Feb 10. doi:10.1128/mSphere.01231-20
Yu L, Chen X, Sun X, et al. The Glycolytic Switch in Tumors: How Many Players Are Involved?. J Cancer，2017, 8(17): 3430-3440. doi: 10.7150/jca.21125.
Chen X, Yi C, Yang MJ, et al. Metabolomics study reveals the potential evidence of metabolic reprogramming towards the Warburg effect in precancerous lesions. J Cancer. 2021;12(5):1563-1574. Published 2021 Jan 10. doi:10.7150/jca.54252
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;6:81. Published 2015 Mar 25. doi:10.3389/fphys.2015.00081
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.
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 Dairy Sci. 2018;101(4):2838-2841. doi:10.3168/jds.2017-13868
Oh HY, Kim BS, Seo SS, et al. The association of uterine cervical microbiota with an increased risk for cervical intraepithelial neoplasia in Korea. Clin Microbiol Infect. 2015;21(7):674.e1-674.e6749. doi:10.1016/j.cmi.2015.02.026
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. Protein Pept Lett. 2018;25(8):783-790. doi:10.2174/0929866525666180809124633
Enisoglu-Atalay V, Atasever-Arslan B, Yaman B, et al. Chemical and molecular characterization of metabolites from Flavobacterium sp. PLoS One. 2018;13(10):e0205817. Published 2018 Oct 17. doi:10.1371/journal.pone.0205817
Crabo AG, Singh B, Nguyen T, Emami S, Gassner GT, Sazinsky MH. Structure and biochemistry of phenylacetaldehyde dehydrogenase from the Pseudomonas putida S12 styrene catabolic pathway. Arch Biochem Biophys. 2017;616:47-58. doi:10.1016/j.abb.2017.01.011
Graham LS, Krass L, Zariffard MR, Spear GT, Mirmonsef P. Effects of Succinic Acid and Other Microbial Fermentation Products on HIV Expression in Macrophages. Biores Open Access. 2013;2(5):385-391. doi:10.1089/biores.2013.0013
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. doi:10.1016/j.ebiom.2019.04.028

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Metabolomic and Microbiota Profiles in Cervicovaginal Lavage Fluid of women with High- Risk Human Papillomavirus Infection

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