bioRxivMicrobiologyPreprint
Long-read shotgun metagenomics identifies distinct urogenital microbial community states associated with functional potential and urinary tract infection during pregnancy
Melline Fontes NoronhaiD, Ben FerneyhoughiD, Matthew J Roddis, Nick J ParkinsoniD, Alan WolfeiD
Abstract
Urinary tract infections (UTIs) are among the most common bacterial infections during pregnancy and are associated with adverse maternal and neonatal outcomes. Although previous studies used enhanced culture, 16S rRNA gene sequencing, and/or short-read shotgun metagenomic sequencing to characterize the urogenital microbiome of adult females, few have assessed the urogenital microbiome during pregnancy and thus this microbiome remains poorly understood. Here, we applied Oxford Nanopore long-read shotgun metagenomic sequencing to characterize urogenital microbial composition, functional potential, metagenome-assembled genomes (MAGs), and the urogenital virome of pregnant females. Taxonomic profiling identified three distinct microbial community states predominated by Lactobacillus crispatus, Gardnerella species, and L. iners. The Gardnerella-predominated community was associated with a higher frequency of UTI during pregnancy. Functional profiles strongly reflected these community states, demonstrating that taxonomic differences were accompanied by distinct functional potential. Genome-resolved analysis recovered 415 MAGs, including 29 high-quality genomes, revealing substantial genomic diversity among urogenital microbes. Viral sequences, predominantly bacteriophages, also were detected in most samples, highlighting an additional component of the pregnancy-associated urogenital microbiome. These findings demonstrate that pregnant females possess distinct urogenital microbial community states and show that they differ not only in taxonomic composition but also in their functional potential.
- organism: human
- assay: metagenomics
- research focus: cell type annotation
- research focus: clustering
- approach: deep learning
- approach: statistical model
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