Disrupted PGR-B and ESR1 signaling underlies defective decidualization linked to severe preeclampsia
- Igenomix Foundation, INCLIVA, Spain
- Igenomix, Spain
- Department of Obstetrics and Gynecology, University and Polytechnic La Fe Hospital, Spain
- Department of Obstetrics and Gynecology, School of Medicine, Valencia University, Spain
- Obstetrics & Gynecology, BIDMC Harvard University, United States
Figures
Figure 1 with 1 supplement
Global RNA-seq transcriptomic results revealed 593 differentially expressed genes (DEGs) in severe preeclampsia (sPE) vs. control samples.
(A) Schematic drawing of the study design used to identify and validate defective decidualization (DD) fingerprinting in sPE. (B) Statistical significance (-log10 FDR) vs. gene expression log2 fold change (FC) is displayed as a volcano plot of global RNA-seq results. Label indicates: downregulated in sPE (blue dots); upregulated in sPE (red dots); not significant genes (grey dots). (C) Heatmap showing the 25 most upregulated and downregulated genes (total = 593; Figure 1—source data 1) of control vs. sPE samples. See also Figure 1—source data 1.
-
Figure 1—source data 1
The 593 statistically differentially expressed genes (false discovery rate [FDR] < 0.05) with at least 1.2-fold change (FC ≥ 1.2) in severe preeclampsia (sPE) vs. control cases obtained from RNA-seq analysis.
- https://cdn.elifesciences.org/articles/70753/elife-70753-fig1-data1-v1.xlsx
Figure 1—figure supplement 1
Transcriptomic analysis based on gestational age at delivery of control samples.
(A) Volcano plot showing there were no significant differentially expressed genes (DEGs) between controls according to gestational age at delivery. Labels show the two criteria that we used to define the DEGs: p-value adjusted (false discovery rate [FDR] < 0.05) and fold-change (FC ≥ 1.2). Legend: not significant (FDR ≥ 0.05). (B) Principal component analysis (PCA) based on 18,476 genes after filtering out lowly expressed genes does not demonstrate clustering based on gestational age.
Figure 2
Defective decidualization (DD) transcriptomics in vitro vs. in vivo.
(A) Common genes between previous in vitro (left) and current in vivo approaches analyzing decidualization (right). Nine genes overlap in both approaches. (B) Box plot showing the average expression of the nine common genes between control (blue boxes) and severe preeclampsia (sPE) (orange boxes) samples. (C) From the 593 differentially expressed genes (DEGs) obtained by global RNA-seq, a subset of 263 DEGs were identified as genes with a human endometrial stromal cell (hESC) origin using the scRNA-seq data published by Wang et al., 2020. See also Figure 2—source data 1.
-
Figure 2—source data 1
The 593 statistically differentially expressed genes (false discovery rate [FDR] < 0.05) with at least 1.2-fold-change (FC ≥ 1.2) in severe preeclampsia (sPE) vs. control cases obtained from RNA-seq analysis.
- https://cdn.elifesciences.org/articles/70753/elife-70753-fig2-data1-v1.xlsx
Figure 3
Severe preeclampsia defective decidualization (sPE-DD) fingerprint composed of 120 differentially expressed genes (DEGs).
(A) Volcano plot showing downregulated (blue) and upregulated (red) genes in sPE from the DD fingerprint. Each point represents one gene; gray points are the rest of the genes obtained in the global RNA-seq analysis. (B) The three most highly downregulated biological process for each major category (red, cell cycle; yellow, DNA damage response; green, cell signaling; blue, cellular response; gray, cell motility; purple, extracellular matrix; pink, immune response; brown, reproductive process). Enrichment index was calculated by -log(p-value). (C) Clustering of DD fingerprint genes shown for reproductive process, response to bacterial molecules, extracellular matrix organization, regulation of receptor signaling, and response to hormones. See also Figure 3—source data 1 and Figure 3—source data 2.
-
Figure 3—source data 1
List of genes selected as defective decidualization signature in severe preeclampsia (sPE) (120 differentially expressed genes [DEGs] with false discovery rate [FDR] < 0.05 and fold-change [FC] ≥ 1.4).
- https://cdn.elifesciences.org/articles/70753/elife-70753-fig3-data1-v1.xlsx
-
Figure 3—source data 2
Biological process Gene Ontology (GO) terms computed by the 120 genes included in the defective decidualization (DD) fingerprinting in severe preeclampsia (sPE) (N, number of genes associated to GO term; DE, number of genes differentially expressed in this GO term; P.DE, unadjusted p-value; FDR, adjusted p-value).
- https://cdn.elifesciences.org/articles/70753/elife-70753-fig3-data2-v1.xlsx
Figure 4
Validation of the defective decidualization (DD) fingerprint in severe preeclampsia (sPE).
(A) Principal component analysis (PCA) based on 120 genes included in the fingerprinting in the training set. Each sample is represented as a colored point (blue, control; orange, sPE). (B) Heatmap dendrogram of expression of the 120 genes included in the final fingerprinting for each sample of the training set (control, n = 12; sPE, n = 17). (C) PCA based on the fingerprinting in the test set. Each sample is represented as a colored point (blue, control; orange, sPE). (D) Heatmap dendrogram of expression of the 120 genes included in the final fingerprinting for each sample of the test set (control, n = 4; sPE, n = 7). See also Figure 4—source data 1.
-
Figure 4—source data 1
List of genes selected as defective decidualization signature in severe preeclampsia (sPE) (120 differentially expressed genes [DEGs] with false discovery rate [FDR] < 0.05 and fold-change [FC] ≥ 1.4).
- https://cdn.elifesciences.org/articles/70753/elife-70753-fig4-data1-v1.xlsx
Figure 5
Estrogen receptor 1 (ER1) and progesterone receptor-B (PR-B) are linked to defective decidualization (DD) fingerprinting in severe preeclampsia (sPE).
(A) Venn diagram displaying genes included in the fingerprinting (120) predominantly expressed in the endometrium based on Human Protein Atlas data that overlap with genes modulated by ESR1 described by Hewitt et al., 2010 and genes associated with PGR silencing described by Mazur et al., 2015. (B) Network showing the connections between proteins codified by DD fingerprinting and the hormonal receptors, ER1 and PR. Shapes indicate different clusters established by String k-means method. Squares, cluster involved in gland morphogenesis and cell migration; circles, cluster involved in extracellular matrix organization and stromal cell differentiation; hexagons; cluster involved in cellular response to DNA damage and regulation of cell cycle. Color gradient indicate gene expression in terms of log2FC. Hub genes are shown with an asterisk. (C-H) Gene expression levels of IHH, MSX2, ESR1, PGR, PGR-A, and PGR-B assessed for sPE (n=13) vs. controls (n=9) by RT-qPCR (gray bars, control; green bars, sPE). RT-qPCR values are expressed as mean± SE. *** p<0.001, ** p<0.01, *p<0.05. (I-J) Tissue sections of control (n=4) and sPE (n=4) endometrium during late secretory phase were immunostained with antibody against ER1 or PR. Nuclei were visualized with DAPI. Scale bar: 50 µM.
Figure 6
Modeling of the molecular mechanism for defective decidualization (DD) in severe preeclampsia (sPE).
(A) Decidualization induced by P4 and E2 in control pregnancy including the interaction of immune response and endothelium. (B) Hypothetical network that could link DD and dysregulated hormone signaling in sPE. All genes were downregulated. Biological processes specified are candidates to be impaired based on functions associated with the observed dysregulation. Red arrows show the downregulation of decidualization modulators.
Author response image 1
Author response image 2
Validation of the DD fingerprint in sPE.
(A) PCA based on 166 genes included in the fingerprinting in the training set. Each sample is represented as a colored point (blue, control; orange, sPE). (B) Heatmap dendogram of expression of the 166 genes included in the final fingerprinting for each sample of the training set (control, n=12; sPE, n=17). Sex of the fetus is represented by color (yellow, female; green, male). (C) PCA based on the fingerprinting in the test set. Each sample is represented as a colored point (blue, control; orange, sPE). (D) Heatmap dendogram of expression of the 166 genes included in the final fingerprinting for each sample of the test set (control, n=4; sPE, n=7). Sex of the fetus is represented by color (yellow, female; green, male).
Tables
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Biological sample (Homo sapiens) | Endometrial biopsies | University and Polytechnic La Fe Hospital (Valencia, Spain) | Freshly isolated from human donors | |
| Antibody | Anti-progesterone receptor antibody [YR85] (rabbit monoclonal anti-human) | Abcam | Cat: AB32085RRID:AB_777452 | Dilution: (1:50) |
| Antibody | Anti-estrogen receptor alpha antibody (mouse monoclonal antibody) | Santa Cruz | Cat: sc-8002RRID:AB_627558 | Dilution: (1:50) |
| Antibody | Goat anti-rabbit IgG H&L (Alexa Fluor 488) (goat polyclonal) | Abcam | Cat: ab150077RRID:AB_2630356 | Dilution: (1:1000) |
| Antibody | Goat anti-mouse IgG (H + L) Cro Alexa Fluor 488 (goat polyclonal) | Invitrogen | Cat: A-11001RRID:AB_2534069 | Dilution: (1:1000) |
| Sequence-based reagent | RT-qPCR primers | This paper | Supplementary file 3 | |
| Commercial assay or kit | QIAsymphony RNA Kit | Qiagen | 931636 | Global RNA-seq library preparation |
| Commercial assay or kit | Illumina TruSeq Stranded mRNA sample prep kit | Illumina | 20020595 | Global RNA-seq library preparation |
| Commercial assay or kit | Kapa SYBR fast qPCR kit | Kapa Biosystems Inc | KK4602 | Global RNA-seq library preparation |
| Commercial assay or kit | TruSeq RNA CD Index Plate (96 indexes, 96 samples) | Illumina | 20019792 | RNA sequencing |
| Commercial assay or kit | NextSeq 500/550 cartridge of 150 cycles | Illumina | FC-404-2002 | RNA sequencing |
| Commercial assay or kit | SuperScript VILO cDNA Synthesis Kit | Thermo Fisher Scientific | 11754250 | RT-qPCR. cDNA preparation |
| Software, algorithm | STAR | Dobin et al., 2013 | URL: http://code.google.com/p/rna-star/RRID:SCR_004463 | RNA-seq analysisRead alignerVersion 2.4.2a |
| Software, algorithm | FastQC | URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/RRID:SCR_014583 | RNA-seq analysisQuality of FASTQ file determinationVersion 0.11.2 | |
| Software, algorithm | SAMtools | Li et al., 2009 | URL: http://htslib.org/RRID:SCR_002105 | RNA-seq analysisSAM and BAM manipulation files Version 1.1 |
| Software, algorithm | HTSeq | Anders et al., 2015 | URL: http://htseq.readthedocs.io/en/release_0.9.1/RRID:SCR_005514 | RNA-seq analysisTo count the number of reads per geneVersion 0.6.1p1 |
| Software, algorithm | BEDtools | Quinlan and Hall, 2010 | URL: https://github.com/arq5x/bedtools2RRID:SCR_006646 | RNA-seq analysisTo obtain gene coverageVersion 2.17.0 |
| Software, algorithm | edgeR | Robinson et al., 2010 | URL: http://bioconductor.org/packages/edgeR/RRID:SCR_012802 | RNA-seq analysisTo analyze differentially expressed genesVersion 3.24.3 |
| Software, algorithm | String | Jensen et al., 2009 | URL: http://string.embl.de/RRID:SCR_005223 | Interaction Network. |
| Software, algorithm | Cytoscape | Shannon et al., 2003 | URL: http://cytoscape.orgSCR_003032 | Interaction Network |
| Software, algorithm | CytoHubba | Chin et al., 2014 | URL: http://apps.cytoscape.org/apps/cytohubbaRRID:SCR_017677 | Interaction Network |
| Other | Custom scripts | URL: https://github.com/mclemente-igenomix/garrido_et_al_2021 | The specific script to run RNA-seq analysis |
Additional files
-
Supplementary file 1
Maternal and neonatal characteristics of endometrial donors.
- https://cdn.elifesciences.org/articles/70753/elife-70753-supp1-v1.docx
-
Supplementary file 2
Biological and technical variables of interest for controlling confounding effects in the RNA-seq analysis.
- https://cdn.elifesciences.org/articles/70753/elife-70753-supp2-v1.xlsx
-
Supplementary file 3
RT-qPCR primers list.
- https://cdn.elifesciences.org/articles/70753/elife-70753-supp3-v1.doc
-
Transparent reporting form
- https://cdn.elifesciences.org/articles/70753/elife-70753-transrepform1-v1.docx
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Disrupted PGR-B and ESR1 signaling underlies defective decidualization linked to severe preeclampsia
eLife 10:e70753.
https://doi.org/10.7554/eLife.70753
