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Article Abstract

The management and prevention of key inflammatory-associated pregnancy complications such as chorioamnionitis and pre-eclampsia is hampered by a lack of early gestation risk screening tools. In a proof-of-principle study we used targeted cell-free RNA analyses of maternal plasma samples from large animal (sheep) and human pregnancy cohorts to develop a minimally invasive screening test for inflammatory markers. This study utilised a preterm sheep model of sterile and bacterial chorioamnionitis. Date-mated ewes received either intraamniotic Saline Control (n = 10) or E.Coli LPS (Sterile chorioamnionitis) with 2 days (n = 9) or 8 days exposure(n = 6). Preterm lambs were delivered at 124 ± 1d gestation. Findings were validated in a bacterial model of chorioamnionitis where ewes were exposed to 7 days of intraamniotic M.Hominis with delivery at 98 d gestion(n = 8) or 128d gestation(n = 8). Maternal blood was collected prior to intervention and at delivery in each group. Random Forest algorithm was used to analyse 8 cell-free RNA(cfRNA) targets related to inflammation in maternal plasma at baseline and delivery, identifying genes that separated animals with or without intrauterine inflammation. Plasma cfRNA data was compared to mRNA expression in placental tissue. Haematological and placental mRNA comparisons were analysed with ANOVA/Tukey HSD/Dunnett T3 tests. Maternal plasma cfRNA findings of intrauterine inflammation were then validated in human plasma samples from a cohort of patients with late onset pre-eclampsia (n = 10) or uncomplicated pregnancies (n = 10). We present data showing that targeted maternal cfRNA assays can accurately identify chorioamnionitis of sterile (AUC 1.0) and infectious (AUC 0.84) origin in a sheep model of pregnancy. Findings were then validated in human maternal plasma samples from patients with late-onset pre-eclampsia in the 1st (AUC = 0.85), 2nd (AUC = 0.90) and 3rd (AUC = 0.82) trimesters. In both sheep and human model systems, cfRNA tests offered high levels of sensitivity and specificity in the absence of overt clinical symptoms. We suggest that further development of this technology may serve as a scalable, rapidly deployed and cost-effective means for predicting major inflammatory conditions in pregnancy.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12271252PMC
http://dx.doi.org/10.1007/s43032-025-01876-wDOI Listing

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