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. 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.

Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning

“…Machine learning applications in disease diagnosis ( Lai et al., 2024 ), complication prediction ( Pax et al., 2024 ), and forecasting of factors such as bacterial drug resistance and predictive models for bacteriophage therapy of Escherichia coli urinary tract infections have demonstrated promising predictive efficacy ( Hu et al., 2023 ; Dixit et al., 2024 ; Keith et al., 2024 ; Nsubuga et al., 2024 ). Additionally, numerous clinical machine learning prediction models have been developed to predict disease prognosis and survival time by collecting large-scale clinical features ( Kogan et al., 2022 ; Li et al., 2022 ; Tang et al., 2022 ; Li et al., 2023 ), demonstrating excellent predictive performance.…”