The vaginal metabolome and microbiota of cervical HPV‐positive and HPV‐negative women: a cross‐sectional analysis

Borgogna, Joanna-Lynn C.; Shardell,; Santori, E.K.; Nelson, TM; Rath, J M; Glover, Elbert D.; Ravel, Jacques; Gravitt, P E; Yeoman, CJ; Brotman, Rebecca M.

doi:10.1111/1471-0528.15981

Cited by 89 publications

(97 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The copyright holder for this preprint this version posted September 1, 2020. ; https://doi.org/10.1101/2020.08.27.20183426 doi: medRxiv preprint metabolomes (30,36,55). Specifically we found that 3-hydroxybutyrate (β-hydroxybutyrate, BHB), a ketone body, was strongly correlated with abundance of dysbiotic bacterial species, such as Streptococcus, Prevotella, Megasphaera, Atopobium and Sneathia, and unexpectedly with one of predominant vaginal Lactobacillus spp., L. iners.…”

Section: Metabolome Immunoproteome and Microbiome Accurately Predicmentioning

confidence: 71%

Integration of multi-omics data improves prediction of cervicovaginal microenvironment in cervical cancer

Bokulich

Łaniewski

Chase

et al. 2020

Preprint

View full text Add to dashboard Cite

Emerging evidence suggests that a complex interplay between human papillomavirus (HPV), microbiota, and the cervicovaginal microenvironment contribute to HPV persistence and carcinogenesis. Integration of multiple omics datasets is predicted to provide unique insight into HPV infection and cervical cancer progression. Cervicovaginal specimens were collected from a cohort (n=100) of Arizonan women with cervical cancer, cervical dysplasia, as well as HPV-positive and HPV-negative controls. Microbiome, immunoproteome and metabolome analyses were performed using 16S rRNA gene sequencing, multiplex cytometric bead arrays, and liquid chromatography-mass spectrometry, respectively. Multi-omics integration methods, including neural networks (mmvec) and Random Forest supervised learning, were utilized to explore potential interactions and develop predictive models. Our integrated bioinformatic analyses revealed that cancer biomarker concentrations were reliably predicted by Random Forest regressors trained on microbiome and metabolome features, suggesting close correspondence between the vaginal microbiome, metabolome, and genital inflammation involved in cervical carcinogenesis. Furthermore, we show that features of the microbiome and host microenvironment, including metabolites, microbial taxa, and immune biomarkers are predictive of genital inflammation status, but only weakly to moderately predictive of cervical cancer state. Different feature classes were important for prediction of different phenotypes. Lipids (e.g. sphingolipids and long-chain unsaturated fatty acids) were strong predictors of genital inflammation, whereas predictions of vaginal microbiota and vaginal pH relied mostly on alterations in amino acid metabolism. Finally, we identified key immune biomarkers associated with the vaginal microbiota composition and vaginal pH (MIF and TNF-alpha), as well as genital inflammation (IL-6, IL-10, leptin and VEGF). Integration of multiple different microbiome "omics" data types resulted in modest increases in classifier performance over classifiers trained on the best performing individual omics data type. However, since the most predictive features cannot be known a priori, a multi-omics approach can still yield insights that might not be possible with a single data type. Additionally, integrating multiple omics datasets provided insight into different features of the cervicovaginal microenvironment and host response. Multi-omics is therefore likely to remain essential for realizing the advances promised by microbiome research.

show abstract

Section: Metabolome Immunoproteome and Microbiome Accurately Predicmentioning

confidence: 71%

Integration of multi-omics data improves prediction of cervicovaginal microenvironment in cervical cancer

Bokulich

Łaniewski

Chase

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Since biogenic amines (thought to be produced by BV-associated bacteria) are known to be higher in women with CST IV 8 , it is not surprising, therefore, that the biogenic amines putrescine and cadaverine were higher in CT mono-and CT/ MG co-infected as compared to uninfected women prior to, but not after adjustment for CST. Biogenic amine production involves specific amino acid decarboxylation reactions, and is a well-described bacterial acid resistance and mitigation mechanism 8,16,32,34,35 . This reduction of vaginal acidity may promote colonization by other BV-associated bacteria or further overgrowth of existing BV-associated bacteria.…”

Section: Discussionmentioning

confidence: 99%

The association of Chlamydia trachomatis and Mycoplasma genitalium infection with the vaginal metabolome

Borgogna

Shardell

Yeoman

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

Chlamydia trachomatis (ct) and Mycoplasma genitalium (MG) are two highly prevalent bacterial sexually transmitted infections (STIs) with a significant rate of co-infection in some populations. Vaginal metabolites are influenced by resident vaginal microbiota, affect susceptibility to sexually transmitted infections (STIs), and may impact local inflammation and patient symptoms. Examining the vaginal metabolome in the context of ct mono (ct+) and ct/MG co-infection (ct+/MG+) may identify biomarkers for infection or provide new insights into disease etiology and pathogenesis. Yet, the vaginal metabolome in the setting of ct infection is understudied and the composition of the vaginal metabolome in ct/MG co-infected women is unknown. therefore, in this analysis, we used an untargeted metabolomic approach combined with 16S rRNA gene amplicon sequencing to characterize the vaginal microbiota and metabolomes of ct+, ct+/MG+, and uninfected women. We found that ct+ and ct+/MG+ women had distinct vaginal metabolomic profiles as compared to uninfected women both before and after adjustment for the vaginal microbiota. this study provides important foundational data documenting differences in the vaginal metabolome between CT+, ct+/MG+ and uninfected women. these data may guide future mechanistic studies that seek to provide insight into the pathogenesis of ct and ct/MG infections. Vaginal metabolites are influenced by resident vaginal microbiota, affect susceptibility to sexually transmitted infections (STIs), and impact local inflammation and symptoms 1,2. An "optimal" vaginal microbiota is dominated by Lactobacillus species, which produce the metabolite lactic acid. Lactic acid lowers the pH of the vaginal microenvironment 1-4 , and, through immunomodulatory and direct inhibitory effects, may protect against acquisition of STIs, including Chlamydia trachomatis (CT) and HIV 2,5-7. In contrast, women with non-optimal microbiota, as epitomized by the clinical condition of bacterial vaginosis (BV), have vaginal microbial communities that are low in Lactobacillus spp.; and are instead colonized by a variety of anaerobes which generally produce very little or no lactic acid. Some of these bacteria produce metabolites such as biogenic amines and short chain fatty acids that may be pro-inflammatory and have been linked with symptoms such as vaginal malodor and discomfort 2,8-12. These metabolites may also increase susceptibility to STIs, including bolstering chlamydial persistence 8,13-16. Women with low-Lactobacillus non-optimal vaginal microbiota have an increased risk for acquisition of STIs and ascending infection (including pelvic inflammatory disease [PID]) 17-21 .

show abstract

“…We thank Zhang et al 1 for their interest in our recent research article. 2 The resident vaginal microbiota has a considerable effect on the overall vaginal metabolome, as was first shown in 2013 by Yeoman et al 3 who demonstrated that the metabolome was associated with the low-Lactobacillus state of bacterial vaginosis, a finding also supported by Srinivasan et al 4 In two subsequent papers, our group has demonstrated that the microbiota contributes between 23 and 31% of the total variation in the vaginal metabolome.…”

Section: Authors' Replymentioning

confidence: 97%

Re: The vaginal metabolome and microbiota of cervical HPV‐positive and HPV‐negative women: a cross‐sectional analysis

Zhang

Chen

Zhang

et al. 2020

BJOG

View full text Add to dashboard Cite

The vaginal metabolome and microbiota of cervical HPV‐positive and HPV‐negative women: a cross‐sectional analysis

Cited by 89 publications

References 67 publications

Integration of multi-omics data improves prediction of cervicovaginal microenvironment in cervical cancer

Integration of multi-omics data improves prediction of cervicovaginal microenvironment in cervical cancer

The association of Chlamydia trachomatis and Mycoplasma genitalium infection with the vaginal metabolome

Re: The vaginal metabolome and microbiota of cervical HPV‐positive and HPV‐negative women: a cross‐sectional analysis

Contact Info

Product

Resources

About