The advent of high-throughput technologies, such as 16s rDNA sequencing, has significantly contributed to expanding our knowledge of the microbiota composition of the genital tract during infections such as Chlamydia trachomatis. The growing body of metagenomic data can be further exploited to provide a functional characterization of microbial communities via several powerful computational approaches. Therefore, in this study, we investigated the predicted metabolic pathways of the cervicovaginal microbiota associated with C. trachomatis genital infection in relation to the different Community State Types (CSTs), via PICRUSt2 analysis. Our results showed a more rich and diverse mix of predicted metabolic pathways in women with a CST-IV microbiota as compared to all the other CSTs, independently from infection status. C. trachomatis genital infection further modified the metabolic profiles in women with a CST-IV microbiota and was characterized by increased prevalence of the pathways for the biosynthesis of precursor metabolites and energy, biogenic amino-acids, nucleotides, and tetrahydrofolate. Overall, predicted metabolic pathways might represent the starting point for more precisely designed future metabolomic studies, aiming to investigate the actual metabolic pathways characterizing C. trachomatis genital infection in the cervicovaginal microenvironment.
Keywords: Chlamydia trachomatis; PICRUSt2; cervicovaginal microbiota; in-silico metabolic profiling; metagenomic data.