Insights Into Vaginal Bacterial Communities and Metabolic Profiles of Chlamydia trachomatis Infection: Positioning Between Eubiosis and Dysbiosis

Abstract

The vaginal microbiota plays a crucial role in maintaining the health and functioning of the female genital tract, preventing the colonization of urogenital pathogens and sexually transmitted infections. In this study, we characterized the vaginal bacterial communities and the metabolome associated to Chlamydia trachomatis infection (CT: 20 women), compared to healthy condition (H: 22 women) and bacterial vaginosis (BV: 19 women). A microarray-based tool (VaginArray), implemented with a real-time PCR for Gardnerella vaginalis, was used to determine the vaginal bacterial composition, whereas the metabolic profiles were assessed by a proton-based nuclear magnetic resonance (¹H-NMR) spectroscopy. CT infection was characterized by bacterial and metabolic signatures similar to healthy condition, even though higher amounts of Lactobacillus iners, as well as depletion of some amino acids, biogenic amines, and succinate marked CT infection. Moreover, the frequency of Lactobacillus crispatus was higher in asymptomatic CT-positive patients than in women with CT-correlated symptoms. We also confirmed the marked differences in the microbiome and metabolome between healthy and BV-affected women. In conclusion, we highlighted microbial and metabolic peculiarities of the vaginal ecosystem in the case of CT infection, even though further studies are needed to understand if the observed alterations precede the infection onset or if the pathogen itself perturbs the vaginal environment.

Keywords: Chlamydia trachomatis; bacterial vaginosis; eubiosis; vaginal metabolome; vaginal microbiota.

FIGURE 1

Heatmap and hierarchical clustering of the VaginArray data. Squares, triangles, and circles represent H, BV, and CT women, respectively. The mean values of the logarithm of the fluorescence intensity for each bacterial group are plotted in a color scale. Maximum distances and Ward’s clustering method were used to create the dendrogram.

FIGURE 2

Principal components analysis (PCA) of the vaginal bacterial communities of H, BV, and CT subjects. PCA was performed on centered and scaled data. (A) Score-plot of PCA. The squares, triangles, and circles represent H, BV, and CT women, respectively. (B) Box plot representing the distribution of PC1 values of the women groups. (C) Pearson correlation coefficients between bacterial groups concentration and PC1 values. To ease the visual interpretation of the values, the –0.5 and 0.5 are remarked by dashed lines and significant correlations (P < 0.01) are depicted with filled bars.

FIGURE 3

Box plot representing the quantification of bacterial genera/species that significantly vary between groups. (A) Quantification of bacteria showing significant variations between CT and H subjects. (B) Quantification of bacteria showing significant variations between BV and H subjects. ^∗P < 0.05; ^∗∗P < 0.01; ^∗∗∗P < 0.001.

FIGURE 4

RPCA model built on the centered and scaled concentrations of the metabolites showing significant differences between groups. (A) Score-plot of RPCA built on the 10 metabolites that varies between CT and H women. Circles and squares represent CT and H subjects, respectively; empty circles represent the median PC score for each group. (B) Pearson correlation coefficients between molecules concentration and PC1 values (comparison CT vs. H). (C) Score-plot of RPCA built on the 40 metabolites that varies between BV and H women. Triangles and squares represent BV and H subjects, respectively; empty circles represent the median PC score for each group. (D) Pearson correlation coefficients between molecules concentration and PC1 values (comparison BV vs. H). Only molecules with a correlation higher than 0.5 or lower than –0.5 are represented. Correlations were statistically significant for all the molecules (P < 0.01).