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Vaginal microbiome and progesterone treatment for preterm birth risk

ABSTRACT: The interaction between the vaginal microbiome, cervical length and vaginal progesterone treatment for preterm birth risk

PROVIDER: PRJEB12577 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	ERR1231780_1.fastq.gz	Fastqsanger.gz
	ERR1231780_2.fastq.gz	Fastqsanger.gz
	ERR1231790_1.fastq.gz	Fastqsanger.gz
	ERR1231790_2.fastq.gz	Fastqsanger.gz
	ERR1231800_1.fastq.gz	Fastqsanger.gz

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Project description:The pregnancy vaginal microbiome contributes to risk of preterm birth, the primary cause of death in children under 5 years of age. Here we describe direct on-swab metabolic profiling by Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) for sample preparation-free characterisation of the cervicovaginal metabolome in two independent pregnancy cohorts (VMET, n = 160; 455 swabs; VMET II, n = 205; 573 swabs). By integrating metataxonomics and immune profiling data from matched samples, we show that specific metabolome signatures can be used to robustly predict simultaneously both the composition of the vaginal microbiome and host inflammatory status. In these patients, vaginal microbiota instability and innate immune activation, as predicted using DESI-MS, associated with preterm birth, including in women receiving cervical cerclage for preterm birth prevention. These findings highlight direct on-swab metabolic profiling by DESI-MS as an innovative approach for preterm birth risk stratification through rapid assessment of vaginal microbiota-host dynamics. Linked cross omic data sets:Meta-taxonomics data associated with this study are available in the European Nucleotide Archive (ENA): accession number <a href='https://www.ebi.ac.uk/ena/browser/view/PRJEB11895' rel='noopener noreferrer' target='_blank'>PRJEB11895</a>, <a href='https://www.ebi.ac.uk/ena/browser/view/PRJEB12577' rel='noopener noreferrer' target='_blank'>PRJEB12577</a> and <a href='https://www.ebi.ac.uk/ena/browser/view/PRJEB41427' rel='noopener noreferrer' target='_blank'>PRJEB41427</a>.

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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.

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Project description:Preterm birth, defined as birth <37 weeks of gestation, is a leading cause of infant morbidity and mortality. In the United States, approximately 12% of all births are preterm.1 Despite decades of research, there has been little progress in developing effective interventions to prevent preterm birth. In fact, the rate of preterm birth has increased slightly over the last several decades.2 The ultimate goal of the Genomic and Proteomic Network for Preterm Birth Research (GPN-PBR) is to identify possible biomarkers that could predict the susceptibility to spontaneous preterm birth (SPTB) as well as to shed light on the molecular mechanisms involved in its etiologies. Understanding those mechanisms will help us predict SPTB and may facilitate the introduction of more effective prevention and treatment strategies.

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Preterm Birth Buccal Cell Epigenetic Biomarkers to Facilitate Preventative Medicine

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