Changes to the cervicovaginal microbiota and cervical cytokine profile following surgery for cervical intraepithelial neoplasia

Abstract

Persistent HPV infection associated with immune modulation may result in high-grade squamous intraepithelial lesions (CIN)2/3. Currently, there is little information on the cervicovaginal microbiome, local cytokine levels and HPV infection related to CIN. Follow-up of patients after local surgery provides an opportunity to monitor changes in the cervicovaginal environment. Accordingly, we undertook this longitudinal retrospective study to determine associations between HPV genotypes, cervicovaginal microbiome and local cytokine profiles in 41 Japanese patients with CIN. Cervicovaginal microbiota were identified using universal 16S rRNA gene (rDNA) bacterial primers for the V3/4 region by PCR of genomic DNA, followed by MiSeq sequencing. We found that Atopobium vaginae was significantly decreased (p < 0.047), whereas A. ureaplasma (p < 0.022) increased after surgery. Cytokine levels in cervical mucus were measured by multiplexed bead-based immunoassays, revealing that IL-1β (p < 0.006), TNF-α (p < 0.004), MIP-1α (p < 0.045) and eotaxin (p < 0.003) were significantly decreased after surgery. Notably, the level of eotaxin decreased in parallel with HPV clearance after surgery (p < 0.028). Thus, local surgery affected the cervicovaginal microbiome, status of HPV infection and immune response. Changes to the cervicovaginal microbiota and cervical cytokine profile following surgery for cervical intraepithelial neoplasia may be important for understanding the pathogenesis of CIN in future.

Figure 1

Heatmap of the relative abundance of the representative microbiota at first and second collections. Cervicovaginal microbiota were collected from 41 patients with CIN and identified by 16S rRNA V3/4 sequencing. First collection included before surgery and observation 1 described in figure. Color gradation indicates the relative abundance of microbiota. The scale of color gradation is indicated at the bottom. The number of microbial species qualified by the observed species richness (Sobs) is indicated as α-diversity in each specimen. Heatmap was drawn by Microsoft Excel.

Figure 2

Symbiotic relationship among microbiota. Spearman’s rank correlation for multiple comparisons was estimated for each taxon as the relative abundance of each symbiont. Color and shade indicate the extent of positive and negative correlation. Dark yellow blocks indicate strong positive correlations (correlation coefficient 0.6–1.0). Pale yellow blocks indicate weak positive correlations (correlation coefficient 0.2–0.6). Pale blue indicates weak negative correlations (correlation coefficient − 0.2 to − 0.6) and dark blue strong negative correlations (correlation coefficient − 0.6 to − 1.0). Correlations were examined by QIIME2.0 (a–c). Lactobacillus species correlations were examined by SpeciateIT (d–f). Correlation significance: *p = 0.01–0.05, **p = 0.001–0.01, ***p < 0.001. Correlation tables were determined using Excel.