Placental TLR recognition of salivary and subgingival microbiota is associated with pregnancy complications

Abstract

Background: Pre-term birth, the leading cause of neonatal mortality, has been associated with maternal periodontal disease and the presence of oral pathogens in the placenta. However, the mechanisms that underpin this link are not known. This investigation aimed to identify the origins of placental microbiota and to interrogate the association between parturition complications and immune recognition of placental microbial motifs. Video Abstract METHODS: Saliva, plaque, serum, and placenta were collected during 130 full-term (FT), pre-term (PT), or pre-term complicated by pre-eclampsia (PTPE) deliveries and subjected to whole-genome shotgun sequencing. Real-time quantitative PCR was used to measure toll-like receptors (TLR) 1-10 expression in placental samples. Source tracking was employed to trace the origins of the placental microbiota.

Results: We discovered 10,007 functionally annotated genes representing 420 taxa in the placenta that could not be attributed to contamination. Placental microbial composition was the biggest discriminator of pregnancy complications, outweighing hypertension, BMI, smoking, and maternal age. A machine-learning algorithm trained on this microbial dataset predicted PTPE and PT with error rates of 4.05% and 8.6% (taxonomy) and 6.21% and 7.38% (function). Logistic regression revealed 32% higher odds of parturition complication (95% CI 2.8%, 81%) for every IQR increase in the Shannon diversity index after adjusting for maternal smoking status, maternal age, and gravida. We also discovered distinct expression patterns of TLRs that detect RNA- and DNA-containing antigens in the three groups, with significant upregulation of TLR9, and concomitant downregulation of TLR7 in PTPE and PT groups, and dense correlation networks between microbial genes and these TLRs. 70-82% of placental microbiota were traced to serum and thence to the salivary and subgingival microbiomes. The oral and serum microbiomes of PTPE and PT groups displayed significant enrichment of genes encoding iron transport, exosome, adhesion, quorum sensing, lipopolysaccharide, biofilm, and steroid degradation.

Conclusions: Within the limits of cross-sectional analysis, we find evidence to suggest that oral bacteria might translocate to the placenta via serum and trigger immune signaling pathways capable of inducing placental vascular pathology. This might explain, in part, the higher incidence of obstetric syndromes in women with periodontal disease.

Keywords: DNA sequence analysis; Metagenomics; Oral microbiome; Placenta; Pre-eclampsia; Pre-term birth; Pregnancy outcomes; Salivary bacteria; Serum; Subgingival.

Fig. 1

Discriminants of the placental microbial assemblages. Principle coordinate analysis (PCoA) of CLR-transformed taxa are shown in A and PCoA of CLR-transformed functional genes in B. In both panels, the data was mapped on smoking status (1), maternal age (2), body mass index (BMI) (3), delivery mode (4), number of pregnancies, i.e., gravida (5), delivery week (6) and parturition outcome (7). The significance of clustering was tested using PERMANOVA

Fig. 2

Differences in microbial community structure and function between full-term delivery, pre-term delivery, and pre-term delivery complicated by pre-eclampsia. Relative abundances of species-level OTUs are shown in each of the 130 women in A. B is a waterfall plot of the functional genes that were most likely to explain differences between classes using LefSe. Each bar represents the effect size (LDA) for a particular gene in a certain group. The length of the bar represents a log10 transformed LDA score. The data supporting this figure is available in Supplementary Table S1

Fig. 3

Bacterial signal recognition in the placenta. Levels of expression of 10 toll-like receptors (TLRs) in the placenta are shown in A. The y-axis represents log(2)-transformed concentrations. Bars with the same symbol are significantly different (p < 0.05, Dunn’s test). Co-occurrence networks between pattern recognition receptors and microbial genes in each group are shown in B–D. Full-term delivery is shown in B, pre-term delivery in C, and pre-term delivery complicated by pre-eclampsia in D. Each network graph contains nodes (circles) and edges (lines). Nodes represent TLRs and KEGG-annotated genes, and edges represent Spearman’s ρ. Edges are colored green for positive correlation and red for negative correlation. Only significant correlations (p < 0.05, t-test) with a coefficient of at least 0.80 are shown

Fig. 4

Differences in salivary and subgingival microbial community structure and function between full-term delivery, pre-term delivery, and pre-term delivery complicated by pre-eclampsia. Linear discriminant analysis (LDA) of CLR-transformed salivary functional genes is shown in A, subgingival genes in B, salivary microbial taxa in C, and subgingival microbial taxa in D. The microbial profiles of subjects clustered by delivery type creating three statistically significant clusters (p = 0.001, MANOVA/Wilks)

Fig. 5

Waterfall plot of subgingival (A) and salivary (B) functional genes that were most likely to explain differences between classes using LefSe. Each bar represents the effect size (LDA) for a particular gene in a certain group. The length of the bar represents a log10 transformed LDA score