Cigarette smoking is associated with an altered vaginal tract metabolomic profile.
Tiffanie Maree NelsonJ C BorgognaR D MichalekD W RobertsJ M RathE D GloverJacques RavelM D ShardellC J YeomanRebecca M BrotmanPublished in: Scientific reports (2018)
Cigarette smoking has been associated with both the diagnosis of bacterial vaginosis (BV) and a vaginal microbiota lacking protective Lactobacillus spp. As the mechanism linking smoking with vaginal microbiota and BV is unclear, we sought to compare the vaginal metabolomes of smokers and non-smokers (17 smokers/19 non-smokers). Metabolomic profiles were determined by gas and liquid chromatography mass spectrometry in a cross-sectional study. Analysis of the 16S rRNA gene populations revealed samples clustered into three community state types (CSTs) ---- CST-I (L. crispatus-dominated), CST-III (L. iners-dominated) or CST-IV (low-Lactobacillus). We identified 607 metabolites, including 12 that differed significantly (q-value < 0.05) between smokers and non-smokers. Nicotine, and the breakdown metabolites cotinine and hydroxycotinine were substantially higher in smokers, as expected. Among women categorized to CST-IV, biogenic amines, including agmatine, cadaverine, putrescine, tryptamine and tyramine were substantially higher in smokers, while dipeptides were lower in smokers. These biogenic amines are known to affect the virulence of infective pathogens and contribute to vaginal malodor. Our data suggest that cigarette smoking is associated with differences in important vaginal metabolites, and women who smoke, and particularly women who are also depauperate for Lactobacillus spp., may have increased susceptibilities to urogenital infections and increased malodor.
Keyphrases
- smoking cessation
- mass spectrometry
- liquid chromatography
- polycystic ovary syndrome
- ms ms
- escherichia coli
- gene expression
- pregnancy outcomes
- metabolic syndrome
- lipopolysaccharide induced
- physical activity
- dna methylation
- high resolution
- tandem mass spectrometry
- pregnant women
- machine learning
- single cell
- copy number
- high performance liquid chromatography
- gas chromatography