Abstract
Preterm birth (PTB) remains a significant global health challenge, affecting millions of pregnancies annually. Despite extensive research, preterm birth rates continue to rise worldwide. The vaginal microbiome composition, a.k.a. microorganisms inhabiting the vaginal ecosystem, influences preterm birth risk and neonatal health outcomes. Variation in the vaginal microbiome composition, particularly decreased lactobacilli abundance and colonization by Group B Streptococcus, correlates with a higher risk of preterm labour. Worryingly, current antibiotic therapies have limitations, including emerging resistance and disturbance of host microbiota balance. Thus, there is a need to investigate predictive and novel preventive strategies to decrease preterm birth rates. As predictive strategies, the present project proposes (a) to explore the already available clinical data using novel bioinformatic tools for the identification of predictive factors (b) to map the vaginal microbiome composition and elucidate its interacting networks (not explored before). In addition, as a preventive strategy, we aim to screen the host lab lactobacilli isolates collection against Group B Streptococcus isolates. Finally, utilizing advanced techniques such as the vagina-on-a-chip model, we will select ten lactobacilli and uncover their interaction with GBS within a physiologically relevant environment, guiding the selection of probiotic candidates.
Researcher(s)
Research team(s)
Project type(s)