Placental microRNA signatures of spontaneous preterm birth
Parenti, M.; Kennedy, E. M.; Firsick, E. J.; Lapehn, S.; MacDonald, J.; Bammler, T.; Enquobahrie, D. A.; LeWinn, K. Z.; Bush, N. R.; McCartney, S. A.; Marsit, C.; Zhao, Q.; Sathyanarayana, S.; Paquette, A. G.
Show abstract
Background: The placenta has a unique transcriptomic profile, including microRNAs that are secreted into maternal circulation throughout pregnancy. MicroRNAs are small, non-coding RNA that post-transcriptionally regulate gene expression. Spontaneous preterm birth (sPTB) is associated with substantial differences in both placental pathophysiology and placental gene expression compared to term birth. We aimed to generate microRNA signatures of sPTB and map them to target genes using a microRNA-mRNA network. Methods: This study was conducted within the Conditions Affecting Neurocognitive Development and Learning in Early childhood (CANDLE) study. Placental samples were collected at delivery, and RNA was isolated for mRNA and microRNA sequencing. To investigate sPTB, this study excluded placental samples of participants with iatrogenic indications for PTB or induced labor. We examined differences in microRNA expression in participants who delivered before 37 weeks (N=35) compared to term participants (N=404) in a series of covariate-adjusted linear regression models. We used paired placental microRNA and mRNA expression data from this cohort to validate associations between computationally predicted microRNA-mRNA pairs and establish a microRNA-mRNA network. Results: Expression of 7 microRNAs were increased in sPTB (FDR<0.05) and were inversely correlated with sPTB-associated genes involved in immune signaling. Expression of 12 microRNAs were decreased in sPTB, including 4 members of the maternally expressed chromosome 14 microRNA cluster (miR-376a-3p, miR-376c-3p, miR-377-3p, and miR-381-3p). These microRNAs were predicted to negatively regulate oxidative phosphorylation genes that were increased in sPTB. The associations between miR-376c-3p and miR-377-3p and oxidative phosphorylation were confirmed in microRNA knockdown experiments. Conclusions: This study highlights potential biological mechanisms by which placental microRNA dysfunction might contribute to sPTB and highlights putative sPTB biomarkers that may be detectable in maternal circulation.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Sex differences in microRNA expression in first and third trimester human placenta 94%
- High-throughput mRNA-seq atlas of human placenta shows vast transcriptome remodeling from first to third trimester 94%
- A mouse model of maternal obesity leads to uterine natural killer (uNK) cell activation and uterine artery remodeling defects 91%
Similar papers in this journal
- Isolated fetal neural tube defects associate with increased risk of placental pathology: evidence from the Collaborative Perinatal Project 95%
- Single-cell sequencing of trophoblasts in preeclampsia and chemical hypoxia in BeWo b30 cells reveals EBI3, COL17A1, miR-27a-5p, and miR-193b-5p as hypoxia-response markers 95%
- Gestational SARS-CoV-2 infection is associated with placental expression of immune and trophoblast genes 94%
Similar papers in this journal
Similar papers in this journal
- Unique transcriptomic landscapes identified in idiopathic spontaneous and infection related preterm births compared to normal term births 95%
- Changes in pregnancy-related serum biomarkers early in gestation are associated with later development of preeclampsia 95%
- Urinary prostaglandin metabolites as biomarkers for human labour: Insights into future predictors 94%
Similar papers in this journal
- Development and Validation of a Paralimbic Related Subcortical Brain Dysmaturation MRI Score in Infants with Congenital Heart Disease 90%
- Genome sequencing and transcriptome profiling in twins discordant for Mayer-Rokitansky-Küster-Hauser syndrome 90%
- Proton pump inhibitors use and risk of preeclampsia 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.