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Biomarker Identification for Preterm Birth Susceptibility: Vaginal Microbiome Meta-Analysis Using Systems Biology and

Sudeepti Kulshrestha1, Priyanka Narad2, Brojen Singh3

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American Journal of Reproductive Immunology (New York, N.Y. : 1989)
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Summary
This summary is machine-generated.

The vaginal microbiome

Keywords:
bioinformaticsmachine learningpregnancy outcomespreterm birthreproductive healthsystems biologyvaginal microbiome

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Area of Science:

  • Microbiome research
  • Genomics
  • Infectious disease

Background:

  • The vaginal microbiome plays a critical role in pregnancy outcomes, including preterm birth (PTB).
  • Existing bioinformatics methods for microbiome analysis have limitations in identifying PTB-associated factors.
  • Understanding these factors is crucial for reducing neonatal mortality worldwide.

Purpose of the Study:

  • To identify key microbial, genetic, and metabolic factors contributing to preterm birth (PTB).
  • To apply an integrated systems biology and machine learning approach for comprehensive analysis.
  • To discover potential targets for preventing PTB.

Main Methods:

  • Analysis of 3757 vaginal microbiome 16S rRNA samples from public datasets.
  • Classification of samples into preterm birth (PTB) and term birth groups.
  • Utilized Parallel-META 3 for taxonomic and functional profiling, followed by systems biology and machine learning for feature identification.

Main Results:

  • Identified nine significant features associated with PTB: Shuttleworthia, Megasphaera, Sneathia, proximal tubule bicarbonate reclamation pathway, systemic lupus erythematosus pathway, transcription machinery pathway, lepA, pepX, and rpoD.
  • Observed variations in the abundance of these features across trimesters.
  • Highlighted specific microbial and metabolic alterations linked to PTB.

Conclusions:

  • Bacterial vaginosis involving Shuttleworthia, Megasphaera, and Sneathia may increase PTB susceptibility.
  • Altered metabolic pathways (e.g., lipopolysaccharide, folate, retinal biosynthesis) are implicated in PTB risk.
  • The identified microbial and molecular features offer potential therapeutic targets for PTB prevention.