Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1080187 | ENA |
REPOSITORIES: ENA
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Project description:Clinical treatment protocols for infertility with in vitro fertilization-embryo transfer (IVF-ET) provide a unique opportunity to assess the human vaginal microbiome in defined hormonal milieu. Herein, we have investigated the association of circulating ovarian-derived estradiol (E2) and progesterone (P4) concentrations to the vaginal microbiome. Thirty IVF-ET patients were enrolled in this study, after informed consent. Blood was drawn at four time points during the IVF-ET procedure. In addition, if a pregnancy resulted, blood was drawn at 4-to-6 weeks of gestation. The serum concentrations of E2 and P4 were measured. Vaginal swabs were obtained in different hormonal milieu. Two independent genome-based technologies (and the second assayed in two different ways) were employed to identify the vaginal microbes. The vaginal microbiome underwent a transition with a decrease in E2 (and/or a decrease in P4). Novel bacteria were found in the vagina of 33% of the women undergoing IVF-ET. Our approach has enabled the discovery of novel, previously unidentified bacterial species in the human vagina in different hormonal milieu. While the relationship of hormone concentration and vaginal microbes was found to be complex, the data support a shift in the microbiome of the human vagina during IVF-ET therapy using standard protocols. The data also set the foundation for further studies examining correlations between IVF-ET outcome and the vaginal microbiome within a larger study population.
2011-10-24 | E-MEXP-2958 | biostudies-arrayexpress
Project description:The goal of this study was to identify amylases that might be present in the vaginal fluid from four individual donors coming either from the microbiome or expressed by the human donors in these fluids. We collected cervicovaginal mucus from 4 donors, characterized the species composition of vaginal communities by genome sequencing. Samples were digested with trypsin, then analyzed by LC-MS/MS. Data was searched with MaxQuant and downstream data analysis was performed using RomicsProcessor.
2020-05-05 | MSV000085378 | MassIVE
Project description:Microbiome sequencing model is a Named Entity Recognition (NER) model that identifies and annotates microbiome nucleic acid sequencing method or platform in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with sequencing metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications
2022-02-21 | MODEL2202170012 | BioModels
Project description:We explore whether a low-energy diet intervention for Metabolic dysfunction-associated steatohepatitis (MASH) improves liver disease by means of modulating the gut microbiome. 16 individuals were given a low-energy diet (880 kcal, consisting of bars, soups, and shakes) for 12 weeks, followed by a stepped re-introduction to whole for an additional 12 weeks. Stool samples were obtained at 0, 12, and 24 weeks for microbiome analysis. Fecal microbiome were measured using 16S rRNA gene sequencing. Positive control (Zymo DNA standard D6305) and negative control (PBS extraction) were included in the sequencing. We found that low-energy diet improved MASH disease without lasting alterations to the gut microbiome.
2024-12-31 | E-MTAB-14061 | biostudies-arrayexpress
Project description:Spontaneous preterm birth (sPTB) is a leading cause of maternal and neonatal morbidity and mortality, yet its prevention and early risk stratification are limited. Previous investigations have suggested that vaginal microbes and metabolites may be implicated in sPTB. Here we performed untargeted metabolomics on 232 second-trimester vaginal samples, 80 from pregnancies ending preterm. We find multiple associations between vaginal metabolites and subsequent preterm birth, and propose that several of these metabolites, including diethanolamine and ethyl glucoside, are exogenous. We observe associations between the metabolome and microbiome profiles previously obtained using 16S ribosomal RNA amplicon sequencing, including correlations between bacteria considered suboptimal, such as Gardnerella vaginalis, and metabolites enriched in term pregnancies, such as tyramine. We investigate these associations using metabolic models. We use machine learning models to predict sPTB risk from metabolite levels, weeks to months before birth, with good accuracy (area under receiver operating characteristic curve of 0.78). These models, which we validate using two external cohorts, are more accurate than microbiome-based and maternal covariates-based models (area under receiver operating characteristic curve of 0.55-0.59). Our results demonstrate the potential of vaginal metabolites as early biomarkers of sPTB and highlight exogenous exposures as potential risk factors for prematurity.
2022-11-21 | MTBLS702 | MetaboLights