Evidence map›Paper›PMID 33123496›Full record

ArticleFrontiers in cellular and infection microbiology2020

A Human Microbiota-Associated Murine Model for Assessing the Impact of the Vaginal Microbiota on Pregnancy Outcomes.

Alexandra A Wolfarth, Taylor M Smith, David VanInsberghe, Anne Lang Dunlop, Andrew S Neish, Elizabeth J Corwin, Rheinallt M Jones

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.3field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 22 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Type 2 diabetes mellitus exacerbates vaginal group BbioRxiv : the preprint server for biology · 2026
    Article
  5. Article
  6. Microbiota-dependent IFN-L controls uterine and placental immunity.bioRxiv : the preprint server for biology · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Frontiers in immunology · 2024
    Article
  12. VaginalbioRxiv : the preprint server for biology · 2023
    Article
  13. Article
  14. Article
  15. Article
  16. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Alexandra A WolfarthDepartment of Pathology, Emory University School of Medicine, Atlanta, GA, United States.
Taylor M SmithDepartment of Pathology, Emory University School of Medicine, Atlanta, GA, United States.
David VanInsbergheDepartment of Pathology, Emory University School of Medicine, Atlanta, GA, United States.
Anne Lang DunlopEmory University Nell Hodgson Woodruff School of Nursing, Emory University School of Medicine, Atlanta, GA, United States.
Andrew S NeishDepartment of Pathology, Emory University School of Medicine, Atlanta, GA, United States.
Elizabeth J CorwinEmory University Nell Hodgson Woodruff School of Nursing, Emory University School of Medicine, Atlanta, GA, United States.
Rheinallt M JonesDivision of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, United States.
Emory University · US

Funding

Implementing a Maternal health and PRegnancy Outcomes Vision for Everyone (IMPROVE)UL1TR002378 · NCATS · EMORY UNIVERSITY · PI Andres J Garcia, Elizabeth O. Ofili · 2017 to 2026
$92.1M
Biobehavioral Determinants of the Microbiome and Preterm Birth in Black WomenR01NR014800 · NINR · EMORY UNIVERSITY · PI CORWIN, ELIZABETH JEANNE, DUNLOP, ANNE LANG · 2013 to 2017
$2.8M
Maintenance and Enhancement of the Atlanta African American Maternal-Child Cohort: Exposome Profiling via High-resolution Metabolomics and Integration of Microbiome-Metabolome-Epigenome DataR24ES029490 · NIEHS · EMORY UNIVERSITY · PI ANNE Lang DUNLOP, Dean Paul Jones · 2018 to 2026
$2.4M
NCATS NIH HHS UL1 TR002378NIEHS NIH HHS R24 ES029490NINR NIH HHS R01 NR014800
6 · The paper itself

Abstract

Disease states are often linked to large scale changes in microbial community structure that obscure the contributions of individual microbes to disease. Establishing a mechanistic understanding of how microbial community structure contribute to certain diseases, however, remains elusive thereby limiting our ability to develop successful microbiome-based therapeutics. Human microbiota-associated (HMA) mice have emerged as a powerful approach for directly testing the influence of microbial communities on host health and disease, with the transfer of disease phenotypes from humans to germ-free recipient mice widely reported. We developed a HMA mouse model of the human vaginal microbiota to interrogate the effects of Bacterial Vaginosis (BV) on pregnancy outcomes. We collected vaginal swabs from 19 pregnant African American women with and without BV (diagnosed per Nugent score) to colonize female germ-free mice and measure its impact on birth outcomes. There was considerable variability in the microbes that colonized each mouse, with no association to the BV status of the microbiota donor. Although some of the women in the study had adverse birth outcomes, the vaginal microbiota was not predictive of adverse birth outcomes in mice. However, elevated levels of pro-inflammatory cytokines in the uterus of HMA mice were detected during pregnancy. Together, these data outline the potential uses and limitations of HMA mice to elucidate the influence of the vaginal microbiota on health and disease.

Indexed as

MicrobiotaVaginosis, BacterialAnimalsDisease Models, AnimalFemaleHumansMicePregnancyPregnancy OutcomeVaginabacterial vaginosis (BV)humanizationinflammationpregnancyvaginal microbiota

Identifiers

PMID33123496
PMCPMC7574503
OpenAlexW3092219824

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.