Persistent infection with high-risk human papillomavirus (HR-HPV) is an important reason for the progression of cervical intraepithelial neoplasia (CIN). Bacterial vaginosis (BV) is a genital infection that frequently presents in women infected with HPV, but the correlation between BV and HPV during CIN development is still elusive. In this study, we enrolled 624 participants and obtained 423 samples of vaginal secretions from them, including 193 HPV-negative samples and 230 HPV-positive samples. We used 16S rRNA sequencing to measure the vaginal microbiota diversity in women with or without BV and HPV co-infection and then calculated risk factors for CIN progression by logistic regression. We found that condom use (OR=3.480; 95% CI=1.069-11.325; P < .05) was a protective factor against CIN, whereas BV (OR=0.358; 95% CI=0.195-0.656; P < .05) and HR-HPV infection (OR= 0.016; 95% CI=0.004-0.072; P < .001) were risk factors for CIN. BV and HPV infection could trigger an increase in the diversity of vaginal microbiota and decrease Lactobacillus domination, which is conducive to CIN regression. The depletion of the carbohydrate metabolism pathway may induce Lactobacillus reduction. Treating BV in the clinical setting could block CIN development and L. iners may be a crucial species during this process.
Research Article
The Correlation Between Bacterial Vaginosis, High-Risk Human Papillomavirus Infection, and Cervical Intraepithelial Neoplasia Progression
https://doi.org/10.21203/rs.3.rs-998392/v1
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Bacterial vaginosis
Vaginal microbiota
16S rRNA sequencing
HPV infection
Cervical lesions
Cervical cancer affects millions of women worldwide, with an estimated incidence of 604 200 new cases and 310 000 deaths in 2020, with China having 109 700 (18.2%) new cases and 59 700 (17.6%) deaths.[1] The prevalence of cervical cancer in China remains higher than in the Western World due to lower vaccine coverage among young women and less awareness of regular cervical cancer screening in adult. Persistent human papillomavirus (HPV) infection causes cervical intraepithelial neoplasia (CIN) that may develop into cervical cancer if not detected in its early stages during screening or treated well once detected. According to a systematic review by Yin et al,[2] the rate of high-risk HPV (HR-HPV) infection caused by types 16, 52, 58, 53, and 18 is up to 19.0% in mainland Chinese women. Infection with HR-HPV is an independent risk factor for CIN and cervical cancer. HPV infection is the most common sexually transmitted infection (STI) especially in women with multiple sex partners.[3] Other factors like smoking, contraception and antibiotic use can disrupt the clearance of HPV leading to a high viral load that can trigger cervical lesions formation. [4]
Bacterial vaginosis (BV) occurs in the reproductive age group with 15–50% of women presenting with vaginal discharge, peculiar smell, itching, or increased vaginal pH levels. [3] There is no standard definition of BV, but it is widely recognized that its development is a process characterized by the dysregulation of vaginal microbiota with increased levels of Gardnerella vaginalis (G. vaginalis), Atopobium, Prevotella, Sneathia, Peptostreptococcus, Megasphera, BV-associated bacterium 1 (BVAB1) to BVAB3, and decreased levels of Lactobacilli spp. [5, 6] The classification of community state types (CSTs) summarizes five types of vaginal microbiota in healthy women based on different domination of bacterium. CST I, II, III, V are dominated by Lactobacillus crispatus (L. crispatus), L. gasseri, L. iners, and L. jensenii, respectively; whereas CST IV is defined as the depletion of Lactobacilli spp. [7–9] Women with CST IV vaginal communities are more likely to experience BV, abortion, preterm labor, increased susceptibility to human immunodeficiency virus (HIV) and HPV infection, or delayed HPV infection clearance. [5, 10] The percentage of women with CST IV cluster is significantly higher in Black and Hispanic women who also have a higher incidence of cervical cancer than that in Asian and White women. [11]
The association between BV and CIN was described 30 years ago, but the mechanism remains elusive. Some research revealed that BV is an independent risk factor for persistent HPV infection. Greater microbiota species diversity is observed in women infected with HPV. The abundances of bacteria associated with BV like Sneathia, Prevotella, and Megashaera are significantly higher in women who are positive for HPV. [6] Previous research focused on clarifying the association between the cervical cytological lesion and BV due to inadequate technological development. With the popularity of colposcopy, recent studies have relied mainly on biopsy under colposcopy, which is more accurate than a thin prep cytology test (TCT), to determine the stage of cervical lesions.
This study is focused on documenting the association of BV, CIN, and HR-HPV infection. We used 16S rRNA gene sequencing to display the vaginal microbiota in Chinese women and compared the composition of microbiota between women with different BV, HPV, or CIN statuses. We aimed to provide some evidence for the importance of managing BV clinically to regress persistent HPV infection that can lead to CIN development.
Participant Enrollment
This study was approved by the Ethics Committee of the Aviation General Hospital, Beijing, China (Ethical approval No. 2021-KY-01-02). Written informed consent was obtained from all participants and all methods were performed in accordance with the guidelines and regulations. Women attending the Aviation General hospital, located in Beijing, China were recruited in our study. The inclusion criteria included: (a.) age range from 25 to 65 years; (b.) untreated BV; (c.) voluntary signing of informed consent form and questionnaire; (d.) no plans to move out of Beijing for two years; and (e.) willingness to be followed up. The exclusion criteria included: (a.) current pregnancy; (b.) undergoing menstrual period; (c.) vaccinated against HPV; (d.) sexual activity, vaginal irrigation or drug application performed 48 hours before sampling; (e.) diagnosed with vulvovaginal candidiasis (VVC), trichomonas vaginitis, or other STIs like Neisseria gonorrhoeae, Chlamydia trachomatis, human immunodeficiency virus (HIV), hepatitis B virus (HBV) or Treponema pallidum; (f.) accompanied by hypertension, diabetes, immune system disease or other diseases; and (g.) antibiotics use in the past 30 days.
Sample Collection
Participants were put in the lithotomy position on the gynecological examination bed after signing the informed consent form and finishing the questionnaire. A well-trained clinical doctor swabbed the vagina using two disposable sterile swabs to collect cervical and vaginal secretions that were then stored in 2 ml saline and 2 ml phosphate buffer saline (PBS, HyClone, USA) tubes, respectively. Samples were kept on ice and transferred to the laboratory for subsequent testing.
BV, HPV, and CIN Diagnosis
An experienced doctor made a diagnosis of BV if a patient met three out of the following four Amsel’s criteria: (a.) increased, thin, and homogeneous vaginal discharge; (b.) vaginal pH greater than 4.5; (c.) presence of clue cells; and (d.) amine odor when potassium hydroxide (KOH) was added to the vaginal secretions. The Hybrid Capture 2 assay (HC2) was used to detect seventeen HR-HPV types (16, 18, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82) and six low-risk types (6, 11, 42, 43, 44, 81). Women who were positive for HR-HPV also accepted TCT to define the cytological lesion. If the results were atypical squamous cells of undetermined significance (ASC-US) or more severe lesions such as low-grade squamous intraepithelial lesions (LSIL) or high-grade squamous intraepithelial lesions (HSIL), they were recommended for colposcopy to define the grade of CIN.
DNA Extraction
FastDNA Spin Kit (MP Biomedicals, USA) was used for DNA isolation from vaginal secretions. We added 200 µl of the samples suspended in saline and 1 ml cell lysis solution (CLS-TC) to Lysing Matrix A (FastPrep) and then followed the protocol. DNA concentration and purity was monitored on 1% agarose gel. According to the concentration, DNA was diluted to 1 ug/ µl using sterile water.
16S rRNA Gene Sequencing
V3-V4 hypervariable fragments of the 16S rRNA gene were amplified using primers 338F (338F: 5’- ACTCCTACGGGAGGCAGCA -3’) and 806R (806R: 5’- GGACTACHVGGGTWTCTAAT -3’) by PCR with the barcode. Mixture PCR products was purified with Qiagen Gel Extraction Kit (Qiagen, Germany). TruSeq DNA PCR-Free sample preparation kits (Illumina, USA) were used for library construction. The library quality was assessed on the [email protected] Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system. At last, the library was sequenced on an Illumina NovaSeq platform (Illumina, CA, USA) and 250bp paired-end reads were generated.
Sequence Analysis
Paired-end reads was assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence. Paired-end reads were merged using FLASH (V1.2.7, http://ccb.jhu.edu/sofeware/FLASH). [31] Quality filtering on the raw tags were performed under specific filtering conditions to obtain the high-quality clean tag according to the QIIME (V1.9.1, http://qiime.org/scripts/split libraries fastq.html) quality controlled process.[32, 33] The tags were compared with the reference database (Silva database, https://www.arb-silva.de/) using UCHIME algorithm (UCHIME Algorithm, http://www.drive5.com/usearch/manual/uchime_algo.html) to detect chimera sequences, and then the chimera sequences were removed. [34] Then the Effective Tags finally obtained. Sequences analyses were performed by Uparse software (Uparse V7.0.1001, http://drive5.com/uparse/). [36] Sequences with≥97% similarity were assigned to the same OTUs. For each representative sequence, the Silva Database (http://www.arb-silva.de/) was used based on Mothur algorithm to annotate taxonomic information. [37] To study phylogenetic relationship of different OTUs and the difference of the dominant species in different samples, multiple sequence alignment were conducted using the MUSCLE software (Version 3.8.31, http://www.drive5.com/muscle/). [38]
Alpha diversity indices Observed-species, Chao1 and Shannon were calculated with QIIME (V1.7.0) and displayed with R software (V3.6.3). Beta diversity was calculated by QIIME software (V1.9.1). Principal Coordinate Analysis (PCoA) analysis was displayed by WGCNA package, stat packages and ggplot2 package in R software. Linear Discriminant Analysis Effect Size (LEfSe) analysis and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States were conducted using Huttenhower lab Galaxy server (http:// www. huttenhower. sph. harvard.edu/galaxy/).
Statistical Analysis
The information collected in the questionnaire was divided into continuous and categorical variables. The continuous variables were presented as mean ± standard deviation (SD), while the categorical variables were displayed by frequencies and proportions. Statistical analyses of continuous variables and categorical variables were performed with Pearson’s chi-squared
test and Wilcoxon rank-sum test, respectively. Logistic regression was used to calculate the value of odds ratio (OR) and assess the risk factors of CIN progression.
All statistical analyses were calculated using SPSS version 23 (IBM, New York, NY) and R software (V3.6.3), and a P value less than .05 was considered statistically significant.
Population Characteristics
Samples were collected from November 2020 to April 17, 2021, from a total of 624 enrolled participants. According to the exclusion criteria (see Methods section), 201 participants were excluded. A total of 423 samples were finally included in this experiment (Table 1). Detailed information was available in the Supplementary material S1, Database 1, Table 1.
According to the recommendations of the American Cancer Society (ACS), women with a cervix should undergo routine cervical cancer screening at least every five years starting at age 25 years until 65 years. We selected women between the age of 25 and 65 years as our participants. There was no significant correlation between HPV infection and postmenopausal status, number of gestations and pregnancies, or use of condoms, but there were significant correlations between age, regular use of contraception, presence of an intrauterine device, and number of sexual partners. Of the 423 samples obtained, 160 out of 423 (37.8%) of the women had BV infections. The HPV-negative group had significantly lower rates of BV infection than the HPV-positive group (P < .001). A total of 230 out of the 423 samples (54.4%) had HR-HPV and 86 out of the 230 samples (37.4%) had more than one type of HPV. Women with HR-HPV who progressed to CIN were 94 out of 230 (40.9%).
Population Characteristics by HPV Status
|
Variables |
HPV-negative (n=193) |
HR-HPV–positive (n=230) |
P value * |
|---|---|---|---|
|
Demographics |
|||
|
Age (y; [IQR]) |
35 (30~41) |
36.5 (31~44) |
0.041*** |
|
Postmenopausal (n [%]) |
13 (6.7) |
18 (7.8) |
0.668 |
|
Pregnancy, mean (±SD) |
1.46 (±1.01) |
1.57 (±1.02) |
0.288 |
|
Gestation, mean (±SD) |
1.03 (±0.67) |
1.07 (±0.73) |
0.676 |
|
Sex behavior characteristics |
|||
|
Contraception (n [%]) |
190 (98.4) |
209 (90.9) |
0.001*** |
|
Condom use (n [%]) ** |
159 (83.7) |
188 (90.0) |
0.063 |
|
IUD implant (n [%]) ** |
29 (15.3) |
18 (8.6) |
0.04*** |
|
Number of sexual partners, mean (±SD) |
1.01 (±0.12) |
1.12 (±0.35) |
0.001*** |
|
Clinical status |
|||
|
BV infection (n [%]) |
54 (28.0) |
106 (46.1) |
0.001*** |
|
Multiple HPV infection (n [%]) |
0 (0) |
86 (37.4) |
0.001*** |
|
CIN (n [%]) |
0 (0) |
94 (40.9) |
0.001*** |
*** statistically significant, P < .05 vs. the HPV-negative group
Next, we conducted logistic regression analysis on possible risk factors for CIN including age, postmenopausal period, number of gestations and pregnancies, methods of contraception, number of sexual partners, BV, senile vaginitis, HPV infection, and multitype HPV infection, which were collected in the questionnaire. We found that condom use (OR=3.480; 95% CI=1.069-11.325; P < .05) was a protective factor for CIN, whereas BV (OR=0.358; 95% CI=0.195-0.656; P < .05) and HR-HPV infection (OR=0.016; 95% CI=0.004-0.072; P < .001) were risk factors for CIN.
The Difference in Composition of Vaginal Microbiota in the HPV-negative and HPV-positive Groups
To identify the difference in composition of vaginal microbiota between women with and without HPV infection, we sequenced the V3-V4 hypervariable region of the 16S rDNA gene utilizing 16S rRNA sequencing. We acquired 15 137 OTUs in our experiment, the sequence result was available in Supplementary material S2, Database 2 and the taxonomy profile was in supplementary S2, Table 1.
In the HPV-negative group, 8 out of 193 samples and 1 out of 230 samples in the HPV-positive group were excluded because of low DNA copy numbers. The relative abundance of the top 19 prevalent vaginal microbiota in HPV-negative and HPV-positive women samples with Lactobacillus spp. predominant in both groups are displayed in Figures 2A and 2B. As shown in Figure 2C, the abundance of Lactobacillus spp. in the HPV-negative group was much higher than that in the HPV-positive group. Meanwhile, microbiota composition in the HPV-positive group was more complicated. Bacteria such as Gardnerella vaginalis, Ralstonia pickettii, Streptococcus anginosus, Prevotella bivia, Prevotella timonensis, Bifidobacterium dentium, and Sneathia sanguinegens presented more frequently in the HPV-positive group. We analyzed the OTUs numbers in each group and displayed them in a Venn graph in Figure 2D. The unique OTUs in the HPV-positive group were significantly higher than in the HPV-negative group. These data suggest that HPV infection could lead to an increase in vaginal microbiota complexity.
The Relative Abundance of Vaginal Microbiota of Different BV, HPV and CIN Statuses
To further estimate the impact of BV and HPV infection on vaginal microbiota changes during CIN progression, we divided all the samples into 6 groups according to the following BV, HPV or CIN statuses: Normal, BV (BV infection), HPV (HR-HPV infection), B.H (BV and HR-HPV co-infection), H.C (HR-HPV infection combined with CIN), and B.H.C (BV and HR-HPV co-infection combined with CIN). To better distinguish the differences between groups, we conducted random screening of the grouped data to balance the sample size of each group. A total of 342 samples were collected and the numbers of final samples of each group were 65, 52, 71, 60, 48, and 46, respectively (Supplementary material S1, Database 1, Table 2, 3, 4, 5, 6 and 7 for detailed information). In Figure 3A, we displayed the top 19 vaginal microbiota at the species level. Lactobacillus spp. had the highest percentage relative abundance of up to 82.6% in the normal group. Women with HPV infection only showed little change in the Lactobacillus spp. genera in their vaginal microbiota, but the percentage of L.crispatus, which presented most in a healthy vaginal environment was partly replaced by L.iners. Lactobacillus spp. and lost its dominance (with less than 60% in composition) in the remaining three groups of women, who were all diagnosed with BV infection with a more complex bacterial composition.
We defined the community state types (CSTs) according to the dominant (> 60% of relative abundance in one sample) Lactobacillus species type. CST I, II, III, V were dominated by L. crispatus, L. gasseri, L. iners, and L. jensenii, respectively, whereas CST IV was defined as the depletion of Lactobacillus spp. Surprisingly, the CST II cluster was only shown in the normal group and CST V in the BV group. The transformation of CST I to CST III was most apparent in the HPV-positive group, which indicated that L.iners may be a key species during HPV infection. Women diagnosed with BV infection were more likely to be defined as being in the CST IV cluster. Interestingly, the percentages of CST IV cluster among BV and HPV co-infected women with CIN were significantly lower than those in the B.H group, and CST III cluster percentage was increased, which also indicates the importance of L.iners.
Higher Microbial Diversity in Women with BV and HPV Co-infection
The observed species, Shannon diversity, and Chao1indexes were used to measure the alpha diversity of microbiomes in 6 groups. High variability was observed among the samples; thus outliers were not shown in the boxplot (Figures. 4A, 4B, and 4C). Three indexes of microbial diversity in the BV, HPV, B.H, and B.H.C groups were all significantly higher than those in the normal group, which indicated that either BV or HPV infection could disturb the balance of vaginal flora (P < .001). Besides, the observed species and the Chao1 index of the H.C group were lower than those of the BV, HPV B.H, and B.H.C groups (P < .001). The Shannon indexes for the HPV and H.C groups were significantly lower than that of the B.H.C group (P < .001). The P values above were all calculated by the Wilcoxon rank-sum test.
The beta diversity was measured with the PCoA based on the Bray–Curtis dissimilarity. All the samples were mainly separated into three groups related to CST clusters [P < .001 (Figure 4D)]. P values were calculated by the ANOSIM test. Biodiversity was not dependent on BV, HPV, or CIN status (supplementary Figure 1).
Important Phylotype in CIN Progression
To find out which bacteria are responsible for the community differences between groups, we used LEfSe to analyze the bacterial diversity and find biomarkers. It combines statistical difference analysis and the impact score of the species on the grouping results while emphasizing statistical significance and biological relevance. Figure 5A and 5B mainly show the species with significant differences in linear discriminant analysis (LDA) score greater than 3.5, which is the marker with a statistical difference. Like a previous study, we found a significant enrichment of Lactobacillus genera in the normal group compared with the group with BV or HPV-infected women. Anaerobic bacteria associated with BV infection such as the Prevotellaceae family, Streptococcaceae family, Atopobiaceae family, and Enterobacteriaceae family were significantly different among BV and HPV co-infected women compared with those in the normal group. These data suggest that CIN development could not be attributed to a single species but the result of multiple species interaction.
Gene Functional Pathways of Bacterial Taxa Associated with BV, HPV or CIN
To better understand the bacterial function during disease progression, we also explored microbiota function using PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States). [12] Gene we detected were matched with the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, predicted raw data was available in Supplementary S4, Database 3. [13,14] The KEGG pathway we tested was presented in supplementary Figure 2. There were many non-human gene pathways in different groups including pathways related to metabolism, genetic information processing, environmental information processing, cellular processes, and organismal systems. There were 33 pathways changes in the B.H.C group compared with the ones with healthy women (P < .05). Pathways related to amino acid, energy, cofactors, vitamin metabolism, biosynthesis of other secondary metabolites, folding, sorting and degradation, and endocrine systems were enriched in the B.H.C group women, whereas carbohydrate metabolism (glycolysis/gluconeogenesis, galactose, and glycerol-lipid metabolism) pathways were depleted in the B.H.C group (Fig 6). Depletion of membrane transporters and carbohydrate metabolism pathways was more frequent in the diseased versus normal groups, which may indicate that bacterial functions were mainly associated with these two pathways.
Studies on the association of vaginal microbiota, HPV infection, and CIN progression have been a hotspot since gene sequencing technology was developed. Vaginal microbiota changes such as depletion of Lactobacillus spp. and increased microbiota diversity are a well-accepted finding in women infected with HPV. [15] We found that the vaginal microbiota in women with BV or HPV infection was more complex and had a reduction of Lactobacillus spp. (Figure 2). This is in line with the current research trends in China and abroad. Condom use (OR=3.480; 95% CI=1.069-11.325; P < .05) was a protective factor, whereas BV (OR=0.358; 95% CI=0.195-0.656; P < .05) and HR-HPV infection (OR=0.016; 95% CI=0.004-0.072; P < .001) were risk factors for CIN in our survey (Figure 1) and other research studies too. [4, 16–18] The prevalence of smoking and combined oral contraceptive (COC) use in Chinese women is lower than in western countries, so we could not make any correlation between them and CIN progression in our cohort.
This study was a cross-sectional analysis based on a large sample size of Chinese women focused on the association of vaginal microbiota disturbance and HPV infection. A surprising finding in our survey was the CST cluster distribution. CST II and V rarely appeared in our cohort especially after HPV infection with or without BV (Figure 3B), which was not consistent with foreign studies done in White, Hispanic, and Black women. [11, 19, 20] This may be because of the ethnic and geographic diversity. [21] The distribution of CST IV cluster in women with CIN was slightly lower than expected probably due to women with severe BV or VVC in whom a colposcopy was not done to prevent the spread of infection. [22, 23] Drugs used to treat the vaginitis may have inhibited the colonization of the vagina by anaerobic bacteria and candida, thus the relative abundance of BV associated bacteria and microbiota diversity in the H.C or B.H.C group (Figures 3A and 4) was slightly lower than in the BV group. The actual relative abundance and diversity could be higher than the available data, but we could conclude that BV-free women (H.C group) have significantly lower vaginal microbiota diversity than the B.H.C group, which is probably conducive to regression of CIN.
Additionally, women infected with HPV have a higher proportion of L.iners than healthy women, which led us to focus on the characteristics of this special Lactobacillus spp. Several studies reveal that L. iners is not only present in the vaginal ecosystem of healthy women, but also of those in a transitional stage or BV state. [24] Genome analyses show the different strains and complicated characteristics of L. iners due to the different morphology and Gram-staining properties. [25 - 28] In 2011, Macklaim et al. [29] used whole-genome sequencing to define the smallest species of L. iners, L. iners AB-1, which is present in both healthy women and those treated with antimicrobials. One of the reasons for its persistence in the vaginal epithelia despite the development of BV and treatment with antibiotics is the presence of fibronectin (Fn)-binding adhesins. [30] The percentage of samples dominated with L. iners was higher in the B.H.C group than in the B.H group (Fig. 3), which makes us question whether L. iners AB-1 was present in our sample. Further research is necessary to detect L. iners AB-1 in abnormal vaginal microbiota flora.
Our study was a cross-sectional analysis conducted in one hospital, which may have limited the observation of the dynamic changes of vaginal microbiota after HPV infection. We chose two specialists to diagnose BV to minimize the misdiagnosis rate, but it is hard to avoid missing the diagnosis of asymptomatic or molecular BV and reporting bias. We only performed 16S rRNA gene sequencing in our study, which failed to distinguish L. iners AB-1 from L. iners in samples. Therefore, further study is needed to better understand vaginal microbiota changes in HPV persistence and CIN progression.
The strengths of this study include the large sample size, which was enough to organize the samples into 6 groups. We also did functional prediction of bacteria, which provides a reference for further mechanism research. We found that BV and HPV co-infected women did not have a higher diversity of vaginal microbiota unless CIN occurred. For future studies, short-, medium- and long-term follow-ups are needed to observe dynamic changes of the vaginal microbiota and the disease process in women with BV or HPV infection. Further investigation is also needed to understand the characteristics of L. iners in both healthy and unhealthy states.
In conclusion, BV and HPV infection could trigger an increase in the diversity of vaginal microbiota, especially BV, which could decrease Lactobacillus spp. domination, which is not conducive to CIN regression. The depletion of carbohydrate metabolism pathways may induce Lactobacillus spp. reduction. Regulating BV in the clinical setting may block CIN development and L. iners may be a crucial species during this process.
DATA AVAILABILITY
All data generated or analyzed in this study are included in this published article and the Supplementary Information.
ACKNOWLEDGEMENT
We would like to thank TopEdit (www.topeditsci.com) for its linguistic assistance during the preparation of this manuscript.
AUTHOR CONTRIBUTIONS
G.G., G.W. and P.L. contributed to conception and design; X.X. and Y.Z. contributed to development of methodology; X.X., Y.Z., L.Y., X.S., M.M., and J.X. contributed to acquisition of data; X.X., Y.Z., and J.P. contributed to analysis and visualization of data; X.X. and Y.Z. contributed to original draft writing; All authors contributed to review and revision of manuscript.
ADDITIONAL INFORMATION
Competing Interests statement
The authors declare no competing interests.
Correspondence and requests for materials should be addressed to G.G.
- Stelzle, D. et al. Estimates of the global burden of cervical cancer associated with HIV. Lancet Glob Health. 2021 Feb;9(2):e161-e169. doi: 10.1016/S2214-109X(20)30459-9. Epub 2020 Nov 16. Erratum in: Lancet Glob Health. 2021 Feb;9(2):e119. PMID: 33212031; PMCID: PMC7815633
- Li, K., Li, Q., Song, L., Wang, D. & Yin, R. The distribution and prevalence of human papillomavirus in women in mainland China. Cancer. 2019 Apr 1;125(7):1030-1037. doi: 10.1002/cncr.32003. Epub 2019 Feb 12. PMID: 30748006
- Coudray, M. S. & Madhivanan, P. Bacterial vaginosis-A brief synopsis of the literature. Eur J Obstet Gynecol Reprod Biol, 245, 143–148 https://doi.org/doi: 10.1016/j.ejogrb.2019.12.035.. (2020 Feb).
- Snijders, P. J., van den Brule, A. J. & Meijer, C. J. The clinical relevance of human papillomavirus testing: relationship between analytical and clinical sensitivity [J]. J Pathol, 201 (1), 1–6 (2003).
- Onderdonk, A. B., Delaney, M. L. & Fichorova, R. N. The Human Microbiome during Bacterial Vaginosis. Clin Microbiol Rev, 29 (2), 223–38 https://doi.org/doi: 10.1128/CMR.00075-15.. (2016 Apr).
- Cheng, L. et al. Vaginal microbiota and human papillomavirus infection among young Swedish women. NPJ Biofilms Microbiomes. 2020 Oct 12;6(1):39. doi: 10.1038/s41522-020-00146-8. PMID: 33046723; PMCID: PMC7552401
- Di Paola, M. et al. Characterization of cervico-vaginal microbiota in women developing persistent high-risk Human Papillomavirus infection.Sci Rep. 2017 Aug31;7(1):10200. doi: 10.1038/s41598-017-09842-6. PMID: 28860468; PMCID: PMC5579045.
- Brotman, R. M. et al. Interplay between the temporal dynamics of the vaginal microbiota and human papillomavirus detection. J Infect Dis. 2014 Dec 1;210(11):1723-33. doi: 10.1093/infdis/jiu330. Epub 2014 Jun 18. PMID: 24943724; PMCID: PMC4296189
- Mitra, A. et al. Cervical intraepithelial neoplasia disease progression is associated with increased vaginal microbiome diversity.Sci Rep. 2015 Nov17;5:16865. doi: 10.1038/srep16865. PMID: 26574055; PMCID: PMC4648063.
- Mitra, A. et al. The vaginal microbiota, human papillomavirus infection and cervical intraepithelial neoplasia: what do we know and where are we going next? Microbiome. 2016 Nov 1;4(1):58. doi: 10.1186/s40168-016-0203-0. PMID: 27802830; PMCID: PMC5088670
- Ravel, J. et al. Vaginal microbiome of reproductive-age women. Proc Natl Acad Sci U S A. 2011 Mar 15;108 Suppl 1(Suppl 1):4680-7. doi: 10.1073/pnas.1002611107. Epub 2010 Jun 3. PMID: 20534435; PMCID: PMC3063603
- Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol, 31 (9), 814–21 https://doi.org/doi: 10.1038/nbt.2676.. (2013 Sep).
- Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000 Jan 1;28(1):27-30. doi: 10.1093/nar/28.1.27. PMID: 10592173; PMCID: PMC102409
- Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci, 28 (11), 1947–1951 https://doi.org/doi: 10.1002/pro.3715.. (2019 Nov).
- Mitra, A. et al. The vaginal microbiota associates with the regression of untreated cervical intraepithelial neoplasia 2 lesions. Nat Commun. 2020 Apr 24;11(1):1999. doi: 10.1038/s41467-020-15856-y. PMID: 32332850; PMCID: PMC7181700
- Gaffney, D. K., Hashibe, M., Kepka, D., Maurer, K. A. & Werner, T. L. Too many women are dying from cervix cancer: Problems and solutions. Gynecol Oncol, 151 (3), 547–554 https://doi.org/doi: 10.1016/j.ygyno.2018.10.004.. (2018 Dec).
- Crosbie, E. J., Einstein, M. H., Franceschi, S. & Kitchener, H. C. Human papillomavirus and cervical cancer. Lancet. 2013 Sep 7;382(9895):889-99. doi: 10.1016/S0140-6736(13)60022-7. Epub 2013 Apr 23. PMID: 23618600
- Kahn, J. A. HPV vaccination for the prevention of cervical intraepithelial neoplasia. N Engl J Med. 2009 Jul 16;361(3):271-8. doi: 10.1056/NEJMct0806938. PMID: 19605832
- Anahtar, M. N. et al. Cervicovaginal bacteria are a major modulator of host inflammatory responses in the female genital tract. Immunity. 2015 May 19;42(5):965-76. doi: 10.1016/j.immuni.2015.04.019. PMID: 25992865; PMCID: PMC4461369
- Torcia, M. G. Interplay among Vaginal Microbiome, Immune Response and Sexually Transmitted Viral Infections. Int J Mol Sci. 2019 Jan 11;20(2):266. doi: 10.3390/ijms20020266. PMID: 30641869; PMCID: PMC6359169
- Muzny, C. A. & Schwebke, J. R. Pathogenesis of Bacterial Vaginosis: Discussion of Current Hypotheses.J Infect Dis. 2016 Aug15;214 Suppl 1(Suppl 1):S1-5. doi: 10.1093/infdis/jiw121. PMID: 27449868; PMCID: PMC4957507.
- Blanks, R. G., Moss, S. M., Coleman, D. A., Addou, S. & Swerdlow, A. J. The use of screening episodes linked to CIN3 and invasive cancer registrations to study outcomes from the NHS Cervical Screening Programme. Cytopathology, 20 (3), 154–60 https://doi.org/doi: 10.1111/j.1365-2303.2008.00620.x.. (2009 Jun).
- Buick, C., Jembere, N., Wang, L. & Kupets, R. Cervical Screening and Colposcopy Management of Women Age 24 and Under. J Obstet Gynaecol Can, 42 (12), 1518–1524 https://doi.org/doi: 10.1016/j.jogc.2020.06.013.. (2020 Dec).
- Petrova, M. I., Reid, G., Vaneechoutte, M. & Lebeer, S. Lactobacillus iners: Friend or Foe? Trends Microbiol. 2017 Mar;25(3):182–191. doi: 10.1016/j.tim.2016.11.007. Epub 2016 Dec 1. PMID: 27914761.
- De Backer, E. et al. Quantitative determination by real-time PCR of four vaginal Lactobacillus species, Gardnerella vaginalis and Atopobium vaginae indicates an inverse relationship between L. gasseri and L. iners.BMC Microbiol. 2007 Dec19;7:115. doi: 10.1186/1471-2180-7-115. PMID: 18093311; PMCID: PMC2233628.
- Lebeer, S., Vanderleyden, J. & De Keersmaecker, S. C. Genes and molecules of lactobacilli supporting probiotic action. Microbiol Mol Biol Rev, 72 (4), 728–64, Table of Contents. https://doi.org/doi: 10.1128/MMBR.00017-08.. (2008 Dec).
- Macklaim, J. M. et al. Comparative meta-RNA-seq of the vaginal microbiota and differential expression by Lactobacillus iners in health and dysbiosis. Microbiome. 2013 Apr 12;1(1):12. doi: 10.1186/2049-2618-1-12. PMID: 24450540; PMCID: PMC3971606
- Yoshimura, K., Ogawa, M. & Saito, M. In vitro characteristics of intravaginal Lactobacilli; why is L. iners detected in abnormal vaginal microbial flora? Arch Gynecol Obstet. 2020 Sep;302(3):671–677. doi: 10.1007/s00404-020-05634-y. Epub 2020 Jun 7. PMID: 32507987.
- Macklaim, J. M., Gloor, G. B., Anukam, K. C., Cribby, S. & Reid, G. At the crossroads of vaginal health and disease, the genome sequence of Lactobacillus iners AB-1. Proc Natl Acad Sci U S A. 2011 Mar 15;108 Suppl 1(Suppl 1):4688–95. doi: 10.1073/pnas.1000086107. Epub 2010 Nov 8. PMID: 21059957; PMCID: PMC3063587.
- McMillan, A., Macklaim, J. M., Burton, J. P. & Reid, G. Adhesion of Lactobacillus iners AB-1 to human fibronectin: a key mediator for persistence in the vagina? Reprod Sci. 2013 Jul;20(7):791–6. doi: 10.1177/1933719112466306. Epub 2012 Nov 30. PMID: 23202727.
- Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011 Nov 1;27(21):2957-63. doi: 10.1093/bioinformatics/btr507. Epub 2011 Sep 7. PMID: 21903629; PMCID: PMC3198573
- Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods, 10 (1), 57–9 https://doi.org/doi: 10.1038/nmeth.2276.. (2013 Jan).
- Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods, 7 (5), 335–6 https://doi.org/doi: 10.1038/nmeth.f.303.. (2010 May).
- Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011 Aug 15;27(16):2194-200. doi: 10.1093/bioinformatics/btr381. Epub 2011 Jun 23. PMID: 21700674; PMCID: PMC3150044
- Haas, B. J. et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons.Genome Res. 2011Mar;21(3):494–504. doi: 10.1101/gr.112730.110. Epub 2011 Jan 6. PMID: 21212162; PMCID: PMC3044863.
- Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods, 10 (10), 996–8 https://doi.org/doi: 10.1038/nmeth.2604.. (2013 Oct).
- Quast, C. et al. Jan;41(Database issue):D590-6. Nucleic Acids Res, https://doi.org/doi: 10.1093/nar/gks1219.. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools
- Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004 Mar 19;32(5):1792-7. doi: 10.1093/nar/gkh340. PMID: 15034147; PMCID: PMC390337
No competing interests reported.
- S1Database1.xlsx
Database 1
- S2Database2amdTable.zip
Database 2
- S4Database3.txt
Database 3
- SupplementaryFigure1.pdf
Supplementary Figure 1
- SupplementaryFigure2.pdf
Supplementary Figure 2