Assembly of Cytoplasmic Stress Granules in Placentas in Women with Preeclampsia

CoCl2-mimicked Hypoxia Induces the Assembly of Stress Granules in Trophoblast Cells Via eIF2α Phosphorylation-dependent and - Independent Pathways

INTRODUCTION

Hypoxia has been implicated in preeclampsia (PE) pathophysiology. Stress granules (SGs) are present in the placenta of patients with PE. However, the pathways that contribute to SG aggregation in PE remain poorly understood.

OBJECTIVE

The objective of the current study is to investigate this issue.

METHODS

We first established an in vitro hypoxia model using human trophoblast cell line HTR-8/SVneo treated with cobalt chloride (CoCl2). CCK8 assay and wound healing assay were conducted to assess the viability and migration of HTR-8/SVneo cells after exposure to CoCl2-mimicked hypoxia. SG component expression in HTR-8/SVneo cells treated with CoCl2 alone, or in combination with indicated siRNAs was evaluated by reverse transcription quantitative PCR (RT-qPCR), western blot and immunofluorescence staining.

RESULTS

Our results found CoCl2-mimicked hypoxia inhibits the proliferation and migration of HTR-8/SVneo cells. The treatment of CoCl2 can induce SG assembly in HTR-8/Svneo cells. Mechanistically, both heme-regulated inhibitors (HRI) mediated eukaryotic translation initiation factor (eIF)2α phosphorylation pathway and 4E binding protein 1 (4EBP1) pathway are involved in SG formation under the stress of CoCl2- mimicked hypoxia.

CONCLUSION

Hypoxia-induced SGs in trophoblast cells might contribute to the etiology of PE.

Progesterone Enhances the Invasion of Trophoblast Cells by Activating PI3K/AKT Signaling Pathway to Prevent Preeclampsia

We aimed to explore whether the effect of progesterone on preeclampsia via the PI3K/AKT signaling pathway. First, we studied the role of progesterone in preeclampsia patients and HTR-8/Svneo cells by adding progesterone. Then PI3K inhibitor LY294002 was added. The effects of progesterone on preeclampsia were also studied in animals by constructing a preeclampsia rat model. CCK-8 and Transwell assay were applied to measure cell viability and invasion ability. ELISA was performed to measure progesterone, MMP-2, MMP-9, pro-inflammatory factors TNF-α, IL-1β, and anti-inflammatory factors IL-4, IL-10, and IL-13 levels. HE staining was used to detect the pathological changes in uterine spiral artery. Western blot was performed to detect Cyclin D1, PCNA, MMP-2, MMP-9, inflammatory factors TNF-α, IL-1β, IL-4, IL-10, IL-13, and PI3K/AKT signaling pathway related proteins AKT, p-AKT, PI3K, and p-PI3K expressions. Progesterone could reduce blood pressure and urine protein in pregnant women with preeclampsia. TNF-α and IL-1β levels were decreased, but IL-4, IL-10, IL-13, cyclin D1, and PCNA levels were increased in pregnant women with preeclampsia after using progesterone. After the use of progesterone, the symptoms of the PE model group were improved. Among them, the lumen of the placental uterine spiral artery was enlarged, and the fibrinoid necrosis of the uterine wall and acute atherosclerotic lesions were relieved. In addition, progesterone promoted HTR-8/Svneo cells proliferation and invasion. However, high expression of MMP-2, MMP-9, p-AKT, and p-PI3K in Normal and preeclampsia groups caused by progesterone was weakened after adding LY294002, indicating that progesterone could activate PI3K/AKT signaling pathway to regulate HTR-8/Svneo cells. Progesterone decreased urine protein and blood pressure of preeclampsia rats in a concentration-dependent manner. Moreover, progesterone activated the PI3K/AKT signaling pathway and inhibited the inflammatory response in preeclampsia rats.

Blood biomarkers representing maternal-fetal interface tissues used to predict early-and late-onset preeclampsia but not COVID-19 infection

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Endothelial dysfunction misleads blood marker discovery by differential expression.

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Blood-derived surrogate transcriptome of target-tissue avoids the false discovery.

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ITGA5 implies polymicrobial infection of maternal-fetal interface in preeclampsia.

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ITGA5 and IRF6 implies viral co-infection in early-onset preeclampsia.

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ITGA5, IRF6, and P2RX7 differ imminent preeclampsia from COVID-19 infection.

Background

A well-known blood biomarker (soluble fms-like tyrosinase-1 [sFLT-1]) for preeclampsia, i.e., a pregnancy disorder, was found to predict severe COVID-19, including in males. True biomarker may be masked by more-abrupt changes related to endothelial instead of placental dysfunction. This study aimed to identify blood biomarkers that represent maternal-fetal interface tissues for predicting preeclampsia but not COVID-19 infection.

Methods

The surrogate transcriptome of tissues was determined by that in maternal blood, utilizing four datasets (n = 1354) which were collected before the COVID-19 pandemic. Applying machine learning, a preeclampsia prediction model was chosen between those using blood transcriptome (differentially expressed genes [DEGs]) and the blood-derived surrogate for tissues. We selected the best predictive model by the area under the receiver operating characteristic (AUROC) using a dataset for developing the model, and well-replicated in datasets both with and without an intervention. To identify eligible blood biomarkers that predicted any-onset preeclampsia from the datasets but that were not positive in the COVID-19 dataset (n = 47), we compared several methods of predictor discovery: (1) the best prediction model; (2) gene sets of standard pipelines; and (3) a validated gene set for predicting any-onset preeclampsia during the pandemic (n = 404). We chose the most predictive biomarkers from the best method with the significantly largest number of discoveries by a permutation test. The biological relevance was justified by exploring and reanalyzing low- and high-level, multiomics information.

Results

A prediction model using the surrogates developed for predicting any-onset preeclampsia (AUROC of 0.85, 95 % confidence interval [CI] 0.77 to 0.93) was the only that was well-replicated in an independent dataset with no intervention. No model was well-replicated in datasets with a vitamin D intervention. None of the blood biomarkers with high weights in the best model overlapped with blood DEGs. Blood biomarkers were transcripts of integrin-α5 (ITGA5), interferon regulatory factor-6 (IRF6), and P2X purinoreceptor-7 (P2RX7) from the prediction model, which was the only method that significantly discovered eligible blood biomarkers (n = 3/100 combinations, 3.0 %; P =.036). Most of the predicted events (73.70 %) among any-onset preeclampsia were cluster A as defined by ITGA5 (Z-score ≥ 1.1), but were only a minority (6.34 %) among positives in the COVID-19 dataset. The remaining were predicted events (26.30 %) among any-onset preeclampsia or those among COVID-19 infection (93.66 %) if IRF6 Z-score was ≥-0.73 (clusters B and C), in which none was the predicted events among either late-onset preeclampsia (LOPE) or COVID-19 infection if P2RX7 Z-score was <0.13 (cluster C). Greater proportions of predicted events among LOPE were cluster A (82.85 % vs 70.53 %) compared to early-onset preeclampsia (EOPE). The biological relevance by multiomics information explained the biomarker mechanism, polymicrobial infection in any-onset preeclampsia by ITGA5, viral co-infection in EOPE by ITGA5-IRF6, a shared prediction with COVID-19 infection by ITGA5-IRF6-P2RX7, and non-replicability in datasets with a vitamin D intervention by ITGA5.

Conclusions

In a model that predicts preeclampsia but not COVID-19 infection, the important predictors were genes in maternal blood that were not extremely expressed, including the proposed blood biomarkers. The predictive performance and biological relevance should be validated in future experiments.