Placenta-derived Exosomes Mitigate Hypoxia-Induced Trophoblast Apoptosis and Inflammatory Progression via SASH1

Summary

SAM and SH3 domain-containing protein 1 (SASH1) is identified as a key regulator of trophoblast apoptosis and inflammation in pre-eclampsia (PE). Placenta-derived exosomes (P-EXOS) alleviate hypoxia-induced injury by suppressing SASH1 expression. These findings reveal a novel mechanism underlying PE pathogenesis and suggest SASH1 as a potential therapeutic target.

Abstract

SASH1 is a signal adaptor protein involved in cell growth, apoptosis, and immune regulation, and has been increasingly studied in tumor and immune cells. Emerging evidence suggests that SASH1 plays an important role in inflammatory responses and cellular homeostasis, processes that are closely associated with the development of PE. This study aimed to determine whether SASH1 contributes to trophoblast apoptosis and inflammatory responses in PE and whether P-EXOS exerts protective effects through SASH1 regulation. In this study, three PE-related transcriptomic datasets (GSE75010, GSE10588, and GSE60438) were analyzed to identify shared differentially expressed genes (DEGs), followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Machine learning algorithms were further applied to screen key candidate genes, and single-cell RNA sequencing data were used to characterize cellular heterogeneity in placental tissue and to determine cell type-specific expression patterns. SASH1 was identified as a consensus candidate gene and was significantly upregulated in trophoblast cells from PE samples. In vitro, a hypoxia-treated HTR-8/SVneo trophoblast cell model was established, combined with SASH1 knockdown, SASH1 overexpression, and co-culture with P-EXOS. Functional experiments showed that knockdown of SASH1 significantly suppressed hypoxia-induced trophoblast apoptosis and reduced the secretion of pro-inflammatory cytokines, including IL-6, IL-1β, and TNF-α, whereas SASH1 overexpression promoted apoptosis and inflammatory responses. In addition, P-EXOS treatment markedly reduced SASH1 expression at both mRNA and protein levels and attenuated hypoxia-induced trophoblast injury, while SASH1 overexpression largely abolished these protective effects. Taken together, these findings indicate that SASH1 plays a critical role in trophoblast apoptosis and inflammatory responses in PE. P-EXOS may alleviate hypoxia-induced trophoblastic injury by suppressing SASH1 expression, providing new insights into the molecular mechanisms and potential therapeutic targets for PE.

Introduction

PE is a pregnancy-specific multisystem disorder that typically occurs after 20 weeks of gestation and is clinically characterized by hypertension, proteinuria, and multi-organ dysfunction. It remains a leading cause of maternal and perinatal morbidity and mortality worldwide¹. Despite continuous advances in clinical management, the definitive treatment for PE is still delivery of the placenta, underscoring the incomplete understanding of its pathogenesis and the urgent need to identify novel molecular and cellular mechanisms as well as potential therapeutic targets^2,3.

Adequate trophoblast invasion and promotion of normal placental development are critical mechanisms underlying the pathogenesis of PE⁴. During normal pregnancy, trophoblast cells invade the uterine decidua and remodel spiral arteries to establish a low-resistance, high-capacity circulation. Impaired invasion and migration of trophoblast cells leads to reduced placental perfusion, resulting in a persistent hypoxic and oxidative stress environment. Hypoxia directly impacts cellular apoptosis, and aberrant release of pro-inflammatory mediators disrupts homeostasis at the maternal-fetal interface, further exacerbating placental dysfunction⁵. Inflammation is also an important driver of PE. IL-17, IL-6, and TNF-α have been found to be markedly elevated in the placenta and peripheral blood of PE patients⁶. Hypoxia and inflammation mutually reinforce each other, forming a vicious cycle that amplifies systemic inflammation, causes endothelial damage, and contributes to placental abnormalities. The underlying molecular networks governing trophoblast apoptosis and inflammatory responses remain largely unknown.

SASH1 is a member of the SLy/SASH1 family of intracellular scaffold proteins that functions as a signaling adaptor⁷. SASH1 has been primarily characterized as a tumor suppressor that inhibits epithelial-mesenchymal transition (EMT), cell migration, and invasion through interactions with signaling partners such as CRKL and the PI3K-Akt-mTOR pathway^8,9. Beyond its tumor-suppressive roles, emerging evidence suggests that SASH1 participates in inflammatory signaling and immune regulation⁷. However, the expression and function of SASH1 in the placenta, particularly in the context of trophoblast biology and PE, have not been investigated.

Exosomes have emerged as important cellular mediators and are being increasingly studied in the context of pregnancy¹⁰. Exosomes transport various bioactive cargos, including proteins, lipids, and noncoding RNAs, to modulate recipient cell function. Increasing evidence suggests that P-EXOS play a critical role in immune regulation during pregnancy, and that their activity and cargo are significantly altered in PE, potentially intensifying the inflammatory state¹¹. Exosomes have also been implicated in regulating oxidative stress¹² and angiogenesis¹³, potentially exerting beneficial effects in pregnancy-related complications. However, their molecular targets and mechanisms of action in PE remain incompletely understood.

It was hypothesized that SASH1 is dysregulated in the PE placenta and functions as a key driver of trophoblast apoptosis and inflammatory responses, and that P-EXOS may exert protective effects against trophoblast injury by modulating SASH1 expression. To test this hypothesis, multiple PE transcriptomic datasets were integrated and single-cell RNA sequencing data with multi-algorithm machine learning to identify key candidate genes. Using a hypoxia-treated HTR-8/SVneo trophoblast cell model and exosome-based intervention experiments, the functional role of SASH1 in trophoblast dysfunction and the therapeutic potential of P-EXOS in PE was investigated.

Protocol

Ethics statement

This study was approved by the Ethics Committee of Shijiazhuang Fourth Hospital (Approval No. 20200031). Written informed consent was obtained from all placental tissue donors prior to sample collection. All procedures involving human participants were conducted in accordance with the Declaration of Helsinki. A complete list of reagents, consumables, equipment, and software used in this protocol is provided in the Table of Materials.

Data collection

RNA-seq data were obtained from the GEO database. The datasets included GSE75010, which contains gene expression data from 157 PE placentas and 173 non-PE placentas (N = 330). GSE10588 contains gene expression data from 26 normal placentas and 17 severe PE placentas (N = 43). GSE60438 contains transcriptome profiling data of decidua basalis from pre-eclamptic patients and normotensive pregnancies (N = 125). Single-cell transcriptome data were obtained from the GEO dataset GSE183338. It includes single-nucleus samples from the chorionic villi/maternal-fetal interface of PE and healthy pregnancies.

DEGs analysis

DEGs associated with PE, the GSE75010, GSE10588, and GSE60438 datasets were first preprocessed and normalized. Then, differential analysis was performed using the R package “limma”¹⁴ based on the sample grouping information. Genes with p < 0.05 and |log2FC| > 0.5 were selected. Volcano plots of DEGs were generated using the R package ggplot2. Heat maps of the top 20 DEGs were drawn using the R package pheatmap. Subsequently, the intersection of the DEGs selected from the three datasets was taken, and a protein-protein interaction (PPI) network was constructed for the candidate genes using the online platform STRING, with an interaction score ≥0.15. The top 20 hub genes were further identified from this PPI network based on their degree of connectivity, ranked using Cytoscape software. The PPI network results were visualized using Cytoscape software or STRING.

Enrichment analysis

Gene enrichment analysis was performed using the ClusterProfiler and DOSE packages in combination with the Metascape website. The databases were obtained from GO and KEGG. Enrichment analysis was conducted using the “EnrichGO” function. Pathways with p < 0.05 were considered significantly enriched. The enrichment results were visualized using the “ggplot2” and “ggpubr” packages.

Machine learning

To identify robust and biologically meaningful DEGs associated with PE, a multi-model machine learning feature selection analysis was performed based on the publicly available transcriptome dataset GSE60438 (platform: GPL6884). This dataset contains expression profiling of decidua basalis samples collected from pre-eclamptic and normotensive pregnancies at Cesarean section. The pre-filtered DEGs were standardized, and the expression matrix, together with corresponding clinical grouping information, was used as input for four distinct machine learning algorithms in order to reduce model bias and enhance the stability of feature selection.

The four algorithms were applied simultaneously, without a specific order. LASSO was performed using the “glmnet” package to conduct regression analysis and select important feature genes. An L1 regularization term was added to the loss function, which shrinks the coefficients of less important features to zero, thereby achieving feature selection. SVM-RFE was implemented using the “e1071” package to construct a support vector machine with recursive feature elimination. A classifier was first trained using SVM, and the least informative features were iteratively removed based on feature weights, yielding an optimal feature subset. XGBoost was applied using the “xgboost” package to build multiple decision trees. Each tree fitted the residuals of the previous tree, and the weighted outputs were accumulated to obtain the final prediction. Boruta was performed using the “randomForest” package, generating shadow features that competed with real features in training a random forest. Features with importance values significantly higher than random noise were retained.

Single-cell transcriptome data analysis

Single-cell transcriptome data were obtained from the GEO database, and the raw count matrix was retrieved from GSE183338. The count matrix was imported using the “Read10X” function of the Seurat package and converted to a dgCMatrix format. Individual objects were merged into a single aggregate object using the “merge” function, and cell labels were made unique using “RenameCells”. Low-quality cells were filtered based on the following criteria: genes expressed in fewer than three cells were removed, and cells expressing fewer than 200 genes were excluded. Quality-controlled cells were normalized and highly variable genes were identified. Global scaling normalization was applied using “LogNormalize” (scale factor = 10,000), highly variable genes (n = 2,000) were selected using “FindVariableFeatures”, and data were scaled using “ScaleData”. Principal component analysis was performed on highly variable features, and the top 30 principal components were retained. Batch effects between samples were corrected using the Harmony method. Cells were visualized and downscaled using UMAP. Shared nearest neighbor graphs were constructed using “FindNeighbors” and “FindClusters” based on the Louvain algorithm. The resolution parameter in “FindClusters” was optimized between 0.1 and 1. The clustering tree was visualized using the “clustree” function, and a resolution of 0.9 was selected to define cell clusters. Potential doublets were removed using the Scrublet algorithm. Cell clusters were annotated by identifying differentially expressed marker genes using the “FindAllMarkers” function. The non-parametric Wilcoxon rank sum test was applied with Bonferroni correction. Cell identities were assigned based on surface markers, relevant literature, and the Cell Classification Database¹⁵.

Cell culture

The trophoblast cell line HTR-8/SVneo cells were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin. Hypoxic conditions were established by culturing cells at 1% O₂, 5% CO₂, and 94% N₂ for 24 h; normoxic controls were maintained at 20% O₂ and 5% CO₂¹⁶. All cell culture procedures should be performed in a Class II biosafety cabinet using aseptic technique. Culture media, transfection reagents, and cell waste should be disposed of in accordance with institutional biosafety guidelines.

Cell transfection

Plasmids containing sh-SASH1, sh-NC, OE-SASH1, and OE-NC were synthesized. HTR-8/SVneo cells were seeded at a density of 5 × 10⁵ cells per well in six-well plates. Cells were subsequently transfected with 2 µg sh-SASH1, sh-NC, OE-SASH1, or OE-NC plasmid per well using a transfection reagent according to the manufacturer's instructions. Briefly, plasmid DNA and P3000 Reagent were diluted in Opti-MEM, mixed with Lipofectamine 3000 diluted separately in Opti-MEM, incubated for 15 min at room temperature, and added to cells at 70–80% confluency. Forty-eight hours post-transfection, SASH1 expression was assessed by RT-qPCR and Western blot. The shRNA target sequences used for SASH1 knockdown are listed in Supplementary Table 1.

Real-time quantitative PCR

Total RNA was extracted from HTR-8/SVneo cells and reverse-transcribed into cDNA using a reverse transcription kit at 42 °C for 30 min, followed by 85 °C for 5 min. Real-time quantitative PCR (qPCR) was performed using SYBR Green master mix with the following cycling conditions: 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. The relative mRNA expression was calculated using the ΔΔCt method, with β-actin as the internal reference. Primer sequences used in this experiment are listed in Supplementary Table 2.

Western blot assay

Total protein was extracted from HTR-8/SVneo cells using the lysis buffer. Cell lysates were collected, incubated on ice, and centrifuged at 12,000 × g for 30 min at 4 °C to remove insoluble debris. Protein concentration was determined using a spectrophotometer. Equal amounts of protein (50 µg) were separated by SDS-PAGE and subsequently transferred onto PVDF membranes. The membranes were blocked with 5% non-fat milk and incubated with primary antibodies overnight at 4 °C. After washing, the membranes were incubated with the corresponding secondary antibodies, and protein bands were visualized using an enhanced chemiluminescence detection system.

For protein detection, primary antibodies included anti-SASH1 and β-actin. Appropriate horseradish peroxidase (HRP)-conjugated secondary antibodies—goat anti-rabbit and goat anti-mouse—were employed. β-actin was used as the internal loading control to ensure equal protein loading. The intensity of protein bands was measured and quantified using ImageJ software.

Isolation of P-EXOS

P-EXOS were isolated from placental villous tissue obtained from term placentas of healthy women undergoing elective cesarean section. Placental villous tissue was washed thoroughly with sterile PBS, minced into approximately 1 mm³ fragments, and cultured in RPMI-1640 medium supplemented with 10% exosome-depleted FBS at 37 °C in 5% CO₂ for 48 h. The conditioned medium was subjected to differential centrifugation as follows: 300 × g for 10 min to remove cells and tissue debris; 2,000 × g for 20 min to remove cell debris; and 10,000 × g for 30 min to remove microvesicles, all at 4 °C. The resulting supernatant was ultracentrifuged at 120,000 × g for 70 min at 4 °C to pellet exosomes. The pellet was washed once with PBS and re-ultracentrifuged at 120,000 × g for 70 min at 4 °C. The final pellet was resuspended in PBS. The isolated exosomes were characterized by Western blot analysis for exosome markers (PLAP, CD63, and TSG101, with GM130 as a negative control) and further examined by transmission electron microscopy for morphological observation.

P-EXOS Cellular Uptake Experiment

To confirm cellular internalization of P-EXOS, exosomes were fluorescently labeled with the lipophilic membrane dye PKH67 according to the manufacturer's protocol. Briefly, P-EXOS were incubated with PKH67 (4 µM) in Diluent C for 5 min at room temperature, and the reaction was quenched with an equal volume of 1% bovine serum albumin (BSA). Labeled exosomes were re-isolated by ultracentrifugation (120,000 × g, 70 min, 4 °C) to remove unbound dye. PKH67-labeled P-EXOS (50 µg/mL) were then added to HTR-8/SVneo cells and co-incubated for 24 h under normoxic or hypoxic (1% O₂) conditions. Cells were subsequently washed three times with PBS, fixed with 4% paraformaldehyde for 15 min, and the nuclei were counterstained with DAPI (1 µg/mL). Internalization of PKH67-labeled exosomes was visualized by confocal laser scanning microscopy (CLSM; excitation 490 nm, emission 502 nm). For functional co-culture experiments, HTR-8/SVneo cells were treated with P-EXOS at a concentration of 50 µg/mL (protein equivalent) in complete RPMI-1640 medium supplemented with 10% exosome-depleted FBS under hypoxic conditions (1% O₂) for 24 h.

Enzyme-linked immunosorbent assay (ELISA)

Cell culture supernatants were collected, and the levels of IL-6, IL-1β, and TNF-α were measured using IL-6 ELISA kit, IL-1β ELISA kit, and TNF-α ELISA kit, respectively, according to the manufacturers' instructions. Absorbance at 450 nm was measured using a microplate reader, and the actual concentrations were calculated from the standard curves.

TdT-mediated dUTP nick-end labeling (TUNEL)

Apoptotic cells were detected using the TUNEL assay kit according to the manufacturer's instructions. Briefly, cells were fixed with 4% paraformaldehyde for 15 min at room temperature, permeabilized with 0.1% Triton X-100 in PBS for 5 min on ice, and incubated with TUNEL reaction mixture for 60 min at 37 °C in the dark. Nuclei were counterstained with DAPI, and TUNEL-positive cells were visualized using a fluorescence microscope and quantified by counting the percentage of TUNEL-positive cells in at least five randomly selected fields per sample.

Statistical analysis

All data were analyzed using R and GraphPad Prism. Continuous variables are presented as mean ±SD. Two-group comparisons were performed using Student’s t-test, whereas multiple-group comparisons were conducted using one-way ANOVA followed by Tukey’s post hoc test. Statistical significance for categorical variables was assessed by the Chi-square test or Fisher’s exact test. Unless otherwise stated, correlations between molecules were calculated using Spearman correlation analysis. Exosome characterization experiments were performed using P-EXOS isolated from three independent placenta donors. Cell-based experiments were performed in three independent biological replicates, representing independent experiments conducted on separate occasions using HTR-8/SVneo cells of different passages, with each replicate using P-EXOS isolated from a different placental donor. A p < 0.05 was considered statistically significant.

Results

Transcriptomic analysis of healthy and PE patients

For gene expression differences between the normal (Normal) group and PE patients, the differential expression by the R package limma in the merged datasets was analyzed. A total of 89 DEGs were identified (p < 0.05 and |log₂FC| > 0.5), 69 upregulated genes and 20 downregulated genes for GSE75010 (Figure 1A). There are 2,097 differentially expressed genes, and 1,121 are u...

Discussion

In this study, SASH1 was identified as a key gene dysregulated in PE through integrated transcriptomic analysis, single-cell sequencing, and multi-algorithm machine learning, an approach that is particularly relevant given that targeted therapies for PE remain unavailable¹⁷. It was demonstrated that SASH1 is significantly upregulated in hypoxic trophoblast cells and specifically elevated in the trophoblast compartment of PE placentas at single-cell resolution. Through loss- and gain-of-function ex...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
RPMI-1640 Medium	Gibco	11875085
Fetal Bovine Serum (FBS)	Gibco	16140071
Exosome-depleted FBS	System Biosciences	EXO-FBS-50A-1
Penicillin/Streptomycin	Thermo Fisher Scientific, CN	15140122
Lipofectamine 3000 Transfection Reagent (with P3000 Reagent)	Thermo Fisher Scientific, CN	L3000150
Opti-MEM Reduced Serum Medium	Thermo Fisher Scientific, CN	31985070
SASH1 shRNA plasmid (HuSH)	origene	TL301836
SASH1 ORF expression plasmid	origene	RC218866
Empty vector control	origene	PS100001
Reverse Transcription Kit	Thermo Fisher Scientific, CN	4368814
PowerUp SYBR Green Master Mix	Thermo Fisher Scientific, CN	A46110
SASH1 primers	Sangon Biotech	Custom synthesized
β-actin primers	Sangon Biotech	Custom synthesized
Cell Lysis Buffer	Beyotime	P0013B
NanoDrop Spectrophotometer	Thermo Fisher Scientific, CN	NanoDrop Ultra
PVDF Membrane	Thermo Fisher Scientific, CN	88518
Anti-SASH1 Antibody	ABclonal	A15248
Anti-β-actin Antibody	Abcam	ab6276
HRP-conjugated Goat Anti-Rabbit/Anti-Mouse Secondary Antibody	Beyotime	A0208
ImageJ Software	NIH (public domain)	ImageJ
PKH67 Green Fluorescent Cell Linker Kit (incl. Diluent C)	Sigma-Aldrich, St. Louis, MO, CN	MINI67
Bovine Serum Albumin (BSA)	Sigma-Aldrich, St. Louis, MO, CN	10711454001
DAPI	Beyotime	C1002
Confocal Laser Scanning Microscope (CLSM)	Leica	Leica TCS SP8
Human IL-6 Quantikine ELISA Kit	R&D Systems	D6050B
Human IL-1β/IL-1F2 DuoSet ELISA	R&D Systems	DY201
Human TNF-α Quantikine ELISA Kit	R&D Systems	DTA00D
Microplate Reader	Thermo Scientific	Multiskan FC
In Situ Cell Death Detection Kit (TUNEL Assay Kit)	Roche	11684795910
Triton X-100	Beyotime	P0096
Paraformaldehyde (4%)	Beyotime	P0099
Fluorescence Microscope	Olympus	BX53
Refrigerated Centrifuge (12,000 × g capacity)	Eppendorf	Eppendorf 5430 R
Ultracentrifuge (120,000 × g capacity)	Beckman Coulter	Optima MAX-XP
R Software	The R Foundation	v4.2.1
GraphPad Prism	GraphPad Software	v10.0
Seurat (R package)	Satija Lab (open-source)	v4.0.4
Harmony (R package, batch correction)	Open-source	v1.2.0
Scrublet (Python package, doublet detection)	Open-source	v0.2.3
clustree (R package)	Open-source	v0.5.1
glmnet (R package, LASSO)	Open-source	v4.1.8
e1071 (R package, SVM-RFE)	Open-source	v1.7-14
xgboost (R package)	Open-source	v1.7.7.1
randomForest (R package, Boruta)	Open-source	v4.7-1.1
Gene Expression Omnibus (GEO) Database	NCBI	https://www.ncbi.nlm.nih.gov/geo/
STRING Database	string-db.org	https://www.string-db.org/
Metascape	metascape.org	https://metascape.org/
Cytoscape Software	Cytoscape Consortium	v3.10.4
ggplot2 (R package)	Open-source	v3.5.1
limma (R package)	Open-source	v3.58.1
pheatmap (R package)	Open-source	v1.0.12
ClusterProfiler (R package)	Open-source	v4.8.3
DOSE (R package)	Open-source	v3.28.2
ggpubr (R package)	Open-source	v0.6.0