Direct on-swab metabolic profiling of vaginal microbiome host interactions during pregnancy and preterm birth.
Pamela PruskiGonçalo D S CorreiaHolly V LewisKatia CapucciniPaolo IngleseDenise ChanRichard G BrownLindsay KindingerYun S LeeAnn SmithJulian MarchesiJulie Anne Kathryn McDonaldSimon J S CameronKate Alexander-HardimanAnna L DavidSarah Jane StockJane E NormanVasso TerzidouT G TeohLynne SykesPhillip R BennettZoltán TakátsDavid A MacIntyrePublished in: Nature communications (2021)
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.
Keyphrases
- preterm birth
- mass spectrometry
- low birth weight
- gestational age
- single cell
- multiple sclerosis
- ms ms
- end stage renal disease
- innate immune
- liquid chromatography
- ejection fraction
- chronic kidney disease
- newly diagnosed
- young adults
- peritoneal dialysis
- oxidative stress
- gas chromatography
- polycystic ovary syndrome
- high resolution
- gene expression
- metabolic syndrome
- machine learning
- high performance liquid chromatography
- pregnant women
- pregnancy outcomes
- big data
- dna methylation
- skeletal muscle
- preterm infants
- deep learning