Dataset Information

Genome and transcriptome profiling of spontaneous preterm birth phenotypes.

ABSTRACT: Preterm birth (PTB) occurs before 37 weeks of gestation. Risk factors include genetics and infection/inflammation. Different mechanisms have been reported for spontaneous preterm birth (SPTB) and preterm birth following preterm premature rupture of membranes (PPROM). This study aimed to identify early pregnancy biomarkers of SPTB and PPROM from the maternal genome and transcriptome. Pregnant women were recruited at the Liverpool Women's Hospital. Pregnancy outcomes were categorised as SPTB, PPROM (≤ 34 weeks gestation, n = 53), high-risk term (HTERM, ≥ 37 weeks, n = 126) or low-risk (no history of SPTB/PPROM) term (LTERM, ≥ 39 weeks, n = 188). Blood samples were collected at 16 and 20 weeks gestation from which, genome (UK Biobank Axiom array) and transcriptome (Clariom D Human assay) data were acquired. PLINK and R were used to perform genetic association and differential expression analyses and expression quantitative trait loci (eQTL) mapping. Several significant molecular signatures were identified across the analyses in preterm cases. Genome-wide significant SNP rs14675645 (ASTN1) was associated with SPTB whereas microRNA-142 transcript and PPARG1-FOXP3 gene set were associated with PPROM at week 20 of gestation and is related to inflammation and immune response. This study has determined genomic and transcriptomic candidate biomarkers of SPTB and PPROM that require validation in diverse populations.

SUBMITTER: Gupta JK

PROVIDER: S-EPMC8770724 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Long non-coding RNAs (lncRNAs) have a much higher cell- and/or tissue-specificity compared to mRNAs in most cases, making them excellent candidates for therapeutic applications to reduce off-target effects. Placental long non-coding RNAs have been investigated in the pathogenesis of preeclampsia (often causing preterm birth (PTB)), but less is known about their role in preterm birth. Preterm birth occurs in 11% of pregnancies and is the most common cause of death among infants in the world. We recently identified that genes that drive circadian rhythms in cells, termed molecular clock genes, are deregulated in maternal blood of women with spontaneous PTB (sPTB) and in the placenta of women with preeclampsia. Next, we focused on circadian genes-correlated long intergenic non-coding RNAs (lincRNAs, making up most of the long non-coding RNAs), designated as circadian lincRNAs, associated with sPTB. We compared the co-altered circadian transcripts-correlated lincRNAs expressed in placentas of sPTB and term births using two published independent RNAseq datasets (GSE73712 and GSE174415). Nine core clock genes were up- or downregulated in sPTB versus term birth, where the RORA transcript was the only gene downregulated in sPTB across both independent datasets. We found that five circadian lincRNAs (LINC00893, LINC00265, LINC01089, LINC00482, and LINC00649) were decreased in sPTB vs term births across both datasets (p ≤ .0222, FDR≤.1973) and were negatively correlated with the dataset-specific clock genes-based risk scores (correlation coefficient r = -.65 ∼ -.43, p ≤ .0365, FDR≤.0601). Gene set variation analysis revealed that 65 pathways were significantly enriched by these same five differentially expressed lincRNAs, of which over 85% of the pathways could be linked to immune/inflammation/oxidative stress and cell cycle/apoptosis/autophagy/cellular senescence. These findings may improve our understanding of the pathogenesis of spontaneous preterm birth and provide novel insights into the development of potentially more effective and specific therapeutic targets against sPTB.

Project description:BackgroundPreterm birth remains a major public health issue affecting 10% of all pregnancies and increases risks of neonatal morbidity and mortality. Approximately 50% to 60% of preterm births are spontaneous, resulting from preterm premature rupture of membranes or preterm labor. The pathogenesis of spontaneous preterm birth is incompletely understood, and prediction of preterm birth remains elusive. Accurate prediction of preterm birth would reduce infant morbidity and mortality through targeted patient referral to hospitals equipped to care for preterm infants. Two previous studies have analyzed cervical microRNAs in association with spontaneous preterm birth and the length of gestation, but the extent to which microRNAs serve as predictive biomarkers remains unknown.ObjectiveThis study aimed to examine associations between cervical microRNA expression and spontaneous preterm birth, with the specific goal of identifying a subset of microRNAs that predict spontaneous preterm birth.Study designWe performed a prospective, nested, case-control study of 25 cases with spontaneous preterm birth and 49 term controls. Controls were matched to cases in a 2:1 ratio on the basis of age, parity, and self-identified race. Cervical swabs were collected at a mean gestational age of 17.1 (4.8) weeks of gestation, and microRNAs were analyzed using a quantitative polymerase chain reaction array. Normalized microRNA expression was compared between cases and controls, and a false discovery rate of 0.2 was applied to account for multiple comparisons. Histopathologic analysis of slides of cervical swab samples was performed to quantify leukocyte burden for adjustment in conditional regression models. We explored the use of Relief-based unsupervised identification of top microRNAs and support vector machines to predict spontaneous preterm birth. We performed microRNA enrichment analysis to explore potential biologic targets and pathways in which up-regulated microRNAs might be involved.ResultsOf the 754 microRNAs on the polymerase chain reaction array, 346 were detected in ≥75% of participants' cervical swabs. Average cervical microRNA expression was significantly higher in cases of spontaneous preterm birth than in controls (P=.01). There were 95 significantly up-regulated individual microRNAs (>2-fold change) in cases of subsequent spontaneous preterm birth compared with term controls (P<.05; q<0.2). Notably, miR-143, miR-30e-3p, and miR-199b were all significantly up-regulated, which is consistent with the 1 previous study of cervical microRNA and spontaneous preterm birth. A Relief-based, novel variable (feature) selection machine learning approach had low-to-moderate prediction accuracy, with an area under the receiver operating curve of 0.71. Enrichment analysis revealed that identified microRNAs may modulate inflammatory cell signaling.ConclusionIn this prospective nested case-control study of cervical microRNA expression and spontaneous preterm birth, we identified a global increase in microRNA expression and up-regulation of 95 distinct microRNAs in association with subsequent spontaneous preterm birth. Larger and more diverse studies are required to determine the ability of microRNAs to accurately predict spontaneous preterm birth, and mechanistic work to facilitate development of novel therapeutic interventions to prevent spontaneous preterm birth is warranted.

Dataset Information

Genome and transcriptome profiling of spontaneous preterm birth phenotypes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets