Small Non-Coding RNAs in the Human Placenta: Regulatory Roles and Clinical Utility

Profiling the cell-specific small non-coding RNA transcriptome of the human placenta

The human placenta is the composite of multiple cell types, each which contributes uniquely to placental function. Small non-coding RNAs (sncRNAs) are regulators of gene expression and can be cell-specific. The sncRNA transcriptome of individual placental cell types has not yet been investigated due to difficulties in their procurement and isolation. Using a custom sequencing method, we explored the expression of seven sncRNA species (miRNA, piRNA, rRNA, scaRNA, snRNA, snoRNA, tRNA) from whole chorionic villi and four major sample-matched FACS-sorted cell type (cytotrophoblast, stromal, endothelial, Hofbauer) samples from 9 first trimester and 17 term placentas. After normalization for technical variables, samples clustered primarily by cell type lineage. No sncRNAs were uniquely expressed by cell type, however, mean expression differed by cell type for 115 sncRNAs. Known placentally-expressed sncRNAs showed differing expression by cell type and trimester. Expression of few sncRNAs varied by sex. Lastly, sample-matched sncRNA expression and DNA methylation correlation was not significant, although high correlation (> R2 ± 0.6) was observed for some sncRNA-CpG pairs. This study represents the first exploration of the sncRNA transcriptome of bulk placental villi and placental cell types, informing about the expression and regulatory patterns underlying human placental development.

Worldwide research trends of pregnancy hypertension in epigenetics field

Introduction

Hypertension in pregnancy (HIP) poses significant health risks for both mothers and infants. Development of HIP is influenced by genetic and environmental factors, with epigenetic modifications partially explaining underlying mechanisms. Bibliometric tools aid researchers in quickly gaining insights into field dynamics and trends.

Methods

In this investigation, we conducted a search for relevant publications in the Web of Science Core Collection database using specific keywords. We employed Citespace and WOSviewer software for analysis of interconnections and co-occurrence of information across publications, countries, authors, institutions, keywords and cited literature. Ultimately, we identified 4,316 research papers on hypertension in pregnancy within the epigenetics domain (HIPE).

Results

Our analysis revealed that China had the highest number of publications (n = 1,353, 31.35%), while the University of Melbourne was the most prolific institution (n = 107, 2.48%). Among author analysis, Tong S emerged as highly productive (n = 41, 0.95%). Preeclampsia (PE) emerged as being extensively studied among various types of HIP. High-frequency keywords associated with HIP mechanisms included oxidative stress, proliferation, apoptosis and invasion. Regarding epigenetics-related terms, DNA methylation, mRNA and ncRNA exhibited distinct heat burst periods. The number of HIPE papers demonstrated an upward trend observed through three stages of growth.

Discussion

Our bibliometric-based study provides novel insights into current research progress on HIP from an epigenetic perspective, serving as a source of new ideas and inspiration for future investigations of HIP diseases.

Profiling the cell-specific small non-coding RNA transcriptome of the human placenta

The human placenta is the composite of multiple cell types, each which contributes uniquely to placental function. Small non-coding RNAs (sncRNAs) are regulators of gene expression and can be cell-specific. The sncRNA transcriptome of individual placental cell types has not yet been investigated due to difficulties in their procurement and isolation. Using a custom sequencing method, we explored the expression of seven sncRNA species (miRNA, piRNA, rRNA, scaRNA, snRNA, snoRNA, tRNA) from whole chorionic villi and four major sample-matched FACS-sorted cell type (cytotrophoblast, stromal, endothelial, Hofbauer) samples from 9 first trimester and 17 term placentas. After normalization for technical variables, samples clustered primarily by cell type lineage. No sncRNAs were uniquely expressed by cell type, however, mean expression differed by cell type for 115 sncRNAs. Known placentally-expressed sncRNAs showed differing expression by cell type and trimester. Expression of few sncRNAs varied by sex. Lastly, sample-matched sncRNA expression and DNA methylation correlation was not significant, although high correlation (> R2 ± 0.6) was observed for some sncRNA-CpG pairs. This study represents the first exploration of the sncRNA transcriptome of bulk placental villi and placental cell types, informing about the expression and regulatory patterns underlying human placental development.

Extracellular vesicles from mouse trophoblast cells: Effects on neural progenitor cells and potential participants in the placenta-brain axis†

The fetal brain of the mouse is thought to be dependent upon the placenta as a source of serotonin (5-hydroxytryptamine; 5-HT) and other factors. How factors reach the developing brain remains uncertain but are postulated here to be part of the cargo carried by placental extracellular vesicles (EV). We have analyzed the protein, catecholamine, and small RNA content of EV from mouse trophoblast stem cells (TSC) and TSC differentiated into parietal trophoblast giant cells (pTGC), potential primary purveyors of 5-HT. Current studies examined how exposure of mouse neural progenitor cells (NPC) to EV from either TSC or pTGC affect their transcriptome profiles. The EV from trophoblast cells contained relatively high amounts of 5-HT, as well as dopamine and norepinephrine, but there were no significant differences between EV derived from pTGC and from TSC. Content of miRNA and small nucleolar (sno)RNA, however, did differ according to EV source, and snoRNA were upregulated in EV from pTGC. The primary inferred targets of the microRNA (miRNA) from both pTGC and TSC were mRNA enriched in the fetal brain. NPC readily internalized EV, leading to changes in their transcriptome profiles. Transcripts regulated were mainly ones enriched in neural tissues. The transcripts in EV-treated NPC that demonstrated a likely complementarity with miRNA in EV were mainly up- rather than downregulated, with functions linked to neuronal processes. Our results are consistent with placenta-derived EV providing direct support for fetal brain development and being an integral part of the placenta-brain axis.

TET2-mediated DNA hydroxymethylation of TGFB1 is related to selective intrauterine growth restriction in monochorionic twin pregnancies

INTRODUCTION

Selective intrauterine growth restriction (sIUGR), which specifically occurs in monochorionic (MC) twins, usually has a poor prognosis and the underlying mechanisms are not well understood. It is an ideal model for exploring epigenetic-modified mechanisms for fetal development in MCDA twins due to eliminating the interference of different heritable backgrounds and intrauterine environments among individuals.

METHODS

The levels of ten-eleven translocation 2 (TET2) and its upstream and downstream targets miR-29b-3p and transforming growth factor beta 1 (TGFB1) were determined using RT‒qPCR, western blotting, and immunohistochemistry. Using TET2 overexpression and knockdown methods, we investigated the role of TET2 in trophoblast functions. The regulatory relationships among TET2, miR-29b-3p, and TGFB1 were explored by cell migration assay, invasion assay, apoptotic ratio assays, Western blot, hMeDIP-qPCR and dual-luciferase assay.

RESULTS

A consistent upregulation of TET2 and TGFB1 was observed in the smaller placental shares compared to the larger placental shares in sIUGR. Gain-of-function studies of TET2 in trophoblasts showed decreased cell invasion and increased apoptosis, whereas loss-of-function studies of TET2 rescued this effect. Mechanistic studies revealed that miR-29b-3p and TGFB1 were the upstream factor and downstream target of TET2, respectively. Furthermore, miR-29b-3p/TET2/TGFB1-smad was identified as a unique axis that regulates trophoblast invasion, migration, and apoptosis in a DNA hydroxymethylation-dependent manner.

DISCUSSION

We elucidated the functional roles of TET2 and DNA hydroxymethylation in trophoblasts and identified a novel DNA regulatory mechanism, providing a basis for further exploration of DNA epigenetic regulatory patterns in sIUGR.

Shared developmental pathways of the placenta and fetal heart

Congenital heart defects (CHD) remain the most common class of birth defect worldwide, affecting 1 in every 110 live births. A host of clinical and morphological indicators of placental dysfunction are observed in pregnancies complicated by fetal CHD and, with the recent emergence of single-cell sequencing capabilities, the molecular and physiological associations between the embryonic heart and developing placenta are increasingly evident. In CHD pregnancies, a hostile intrauterine environment may negatively influence and alter fetal development. Placental maldevelopment and dysfunction creates this hostile in-utero environment and may manifest in the development of various subtypes of CHD, with downstream perfusion and flow-related alterations leading to yet further disruption in placental structure and function. The adverse in-utero environment of CHD-complicated pregnancies is well studied, however the specific etiological role that the placenta plays in CHD development remains unclear. Many mouse and rat models have been used to characterize the relationship between CHD and placental dysfunction, but these paradigms present substantial limitations in the assessment of both the heart and placenta. Improvements in non-invasive placental assessment can mitigate these limitations and drive human-specific investigation in relation to fetal and placental development. Here, we review the clinical, structural, and molecular relationships between CHD and placental dysfunction, the CHD subtype-dependence of these changes, and the future of Placenta-Heart axis modeling and investigation.

Identification of novel cell-free RNAs in maternal plasma as preterm biomarkers in combination with placental RNA profiles

BACKGROUND

Preterm birth (PTB) is the main driver of newborn deaths. The identification of pregnancies at risk of PTB remains challenging, as the incomplete understanding of molecular mechanisms associated with PTB. Although several transcriptome studies have been done on the placenta and plasma from PTB women, a comprehensive description of the RNA profiles from plasma and placenta associated with PTB remains lacking.

METHODS

Candidate markers with consistent trends in the placenta and plasma were identified by implementing differential expression analysis using placental tissue and maternal plasma RNA-seq datasets, and then validated by RT-qPCR in an independent cohort. In combination with bioinformatics analysis tools, we set up two protein-protein interaction networks of the significant PTB-related modules. The support vector machine (SVM) model was used to verify the prediction potential of cell free RNAs (cfRNAs) in plasma for PTB and late PTB.

RESULTS

We identified 15 genes with consistent regulatory trends in placenta and plasma of PTB while the full term birth (FTB) acts as a control. Subsequently, we verified seven cfRNAs in an independent cohort by RT-qPCR in maternal plasma. The cfRNA ARHGEF28 showed consistence in the experimental validation and performed excellently in prediction of PTB in the model. The AUC achieved 0.990 for whole PTB and 0.986 for late PTB.

CONCLUSIONS

In a comparison of PTB versus FTB, the combined investigation of placental and plasma RNA profiles has shown a further understanding of the mechanism of PTB. Then, the cfRNA identified has the capacity of predicting whole PTB and late PTB.

Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization

The placenta is crucial to fetal well-being and it plays a significant role in the pathogenesis of hypertensive pregnancy disorders. Moreover, a timely diagnosis of placenta previa may save lives. Ultrasound is the primary imaging modality in pregnancy, but high-quality imaging depends on the access to equipment and staff, which is not possible in all settings. Convolutional neural networks may help standardize the acquisition of images for fetal diagnostics. Our aim was to develop a deep learning based model for classification and segmentation of the placenta in ultrasound images. We trained a model based on manual annotations of 7,500 ultrasound images to identify and segment the placenta. The model's performance was compared to annotations made by 25 clinicians (experts, trainees, midwives). The overall image classification accuracy was 81%. The average intersection over union score (IoU) reached 0.78. The model's accuracy was lower than experts' and trainees', but it outperformed all clinicians at delineating the placenta, IoU = 0.75 vs 0.69, 0.66, 0.59. The model was cross validated on 100 2nd trimester images from Barcelona, yielding an accuracy of 76%, IoU 0.68. In conclusion, we developed a model for automatic classification and segmentation of the placenta with consistent performance across different patient populations. It may be used for automated detection of placenta previa and enable future deep learning research in placental dysfunction.