1. Chromosomes and Gene Expression
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Cardiovascular disease risk factors induce mesenchymal features and senescence in mouse cardiac endothelial cells

  1. Karthik Amudhala Hemanthakumar
  2. Shentong Fang
  3. Andrey Anisimov
  4. Mikko I Mäyränpää
  5. Eero Mervaala
  6. Riikka Kivelä  Is a corresponding author
  1. Wihuri Research Institute, Finland
  2. Stem cells and Metabolism Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, Finland
  3. Translational Cancer Medicine Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, Finland
  4. Pathology, Helsinki University and Helsinki University Hospital, Finland
  5. Department of Pharmacology, Faculty of Medicine, University of Helsinki, Finland
Research Article
Cite this article as: eLife 2021;10:e62678 doi: 10.7554/eLife.62678
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8 figures, 1 table and 1 additional file

Figures

Figure 1 with 3 supplements
Effects of exercise training, aging, obesity, and pressure overload on cardiac endothelial cell (EC) number and vascular density.

(A and B) Fluorescence-activated cell sorting (FACS) analysis and quantification of mean fluorescence intensity (MFI) of the cardiac ECs (CD31+CD140a-CD45-Ter119-DAPI-) in various mouse models. (C and D) Representative immunofluorescence images and quantification of CD31+ blood vessel area (%) in the heart. Scale bar 100 μm. Data is presented as mean ± SEM. Student’s t-test was used, *p<0.05, **p<0.01, ***p<0.001. In panel (B), each color-coded circle indicates an individual biological sample. In panel (D), the number of mice in each experimental group is indicated in the respective graph, N = 3–5 male mice/group.

Figure 1—source data 1

Source data for Figure 1B and D.

https://cdn.elifesciences.org/articles/62678/elife-62678-fig1-data1-v2.xlsx
Figure 1—source data 2

Echocardiography measurements of cardiac function and ventricular dimensions in the indicated experimental group.

https://cdn.elifesciences.org/articles/62678/elife-62678-fig1-data2-v2.pdf
Figure 1—figure supplement 1
Schematic of the experimental set-up to elucidate the impact of cardiovascular disease (CVD) risk factors on cardiac endothelial cell (EC) transcriptome and the validation of the experimental CVD risk factor models.

(A) Experimental workflow demonstrating the mouse models used to mimic CVD risk factors in C57Bl/6J mice, analysis and isolation of cardiac ECs by fluorescence-activated cell sorting, bioinformatic analyses of the cardiac EC transcriptome, and identification and validation of candidate genes using human ECs and heart tissue. (B) Experimental timeline of exercise training (6 weeks of treadmill running), high-fat diet (HFD; 14 weeks of high-fat feeding), physiological aging (18 months old), and pressure overload-induced heart failure by transaortic constriction in mice. (C–F) Ejection fraction in each of the four experiments. (G) Body weight (g) during the HFD experiment. (H) Blood glucose levels during oral glucose tolerance test (mmol/L), and (I) total body fat (%) measured after 14 weeks of HFD. Data is presented as mean ± SEM. Student’s t-test was used, *p<0.05, **p<0.01, ***p<0.001 (in panels C–F and I, the number of mice in each experimental group is indicated in the respective graphs; in panels G–H, N = 4–5 male mice/group were analyzed).

Figure 1—figure supplement 2
Fluorescence-activated cell sorting (FACS) analysis of cardiac endothelial cell (EC).

(A–D) Representative pseudocolor FACS plots showing the gating and percentage of cardiac ECs (CD31+ CD140a- CD45- Ter119- DAPI-) in the different treatment groups. In panels (A–D), the number of male mice in each experimental group is indicated in the respective FACS plots.

Figure 1—figure supplement 3
Quality metrics of the fluorescence-activated cell sorting (FACS) sorted cardiac endothelial cell (EC).

(A) Gating strategy to sort cardiac ECs (CD31+ CD140a- CD45- Ter119- DAPI-). (B) Purity analysis of the post sort EC fraction by QPCR (N = 3–4 male mice/group were analyzed). (C) Representative image of the bioanalyzer data showing the RNA integrity number (RIN) values of the isolated RNA. (D) Mononuclear cells and pie chart of the post sort EC fraction showing viable cells in green and dead cells in red (N = 5 male mice/group were analyzed).

Figure 2 with 2 supplements
Transcriptomic changes in cardiac endothelial cells (ECs) in exercise trained, aged, obese, and transverse aortic constriction (TAC)-treated mice.

(A–E) MA-plots (log ratio over mean) showing the number of differentially expressed genes (DEGs) in cardiac ECs for each experiment. Number of significantly up- and downregulated genes with the false discovery rate (FDR; Benjamini–Hochberg adjusted p-value) threshold of 0.05 are indicated in the plots. (F–J) Top 50 DEGs in cardiac ECs of the indicated experimental groups. In the heatmap, each color-coded circle (red, green, and black) indicates an individual biological sample within each experimental group. N = 3–4 male mice/group.

Figure 2—figure supplement 1
Principal component analysis (PCA) plot and unsupervised hierarchical clustering of cardiac endothelial cell transcriptome from exercise trained, aged, obese, and transverse aortic constriction (TAC)-treated mice.

(A–E) Two-dimensional PCA. (F–J) Unsupervised hierarchical clustering of cardiac endothelial transcriptome in the indicated experimental groups. Each color-coded circle (red, green, and black) in the PCA and unsupervised clustering plot indicate one biological sample and N = 3–4 male mice/experimental condition were analyzed.

Figure 2—figure supplement 2
Dispersion mean plot and p-value distribution plot of the indicated RNA sequencing experiments.

(A–E) Plot of dispersion estimates at different count levels, showing black dot (dispersion estimate for each gene as obtained by considering the information from each gene separately), red line (fitted estimates showing the dispersions' dependence on the mean), blue dot (the final dispersion estimates shrunk from the gene-wise estimates toward the fitted estimates. The values are used for further statistical testing). Blue circles are genes which have high gene-wise dispersion estimates and are hence labeled dispersion outliers and not shrunk toward the fitted trend line. (F–J) Plot of the raw p-value (Wald test) indicated in blue bars and the false discovery rate (FDR) distribution or adjusted p-value (Benjamini–Hochberg adjusted p-value) of the statistical test. The arrow indicates cutoff point FDR threshold of 0.05 and the genes with FDR values less than or equal to the cutoff points were used for further analysis.

Figure 3 with 3 supplements
Cardiovascular disease (CVD) risk factors activate mesenchymal gene expression in cardiac endothelial cells (ECs).

(A) Gene ontology analysis of the up- and downregulated genes. Note the opposite changes induced by exercise training compared to the CVD risk factors. (B–F) Heatmaps showing the differential gene expression of endothelial and mesenchymal genes previously associated with endothelial-to-mesenchymal transition (EndMT). Genes are selected based on published data sets (references are found in Figure 3—source data 1). In all panels, the up- and downregulated genes with the false discovery rate (FDR; Benjamini–Hochberg adjusted p-value) threshold of 0.05 were considered. In the heatmap, each color-coded circle (red, green, and black) indicates an individual biological sample within each experimental group. N = 3–4 male mice/group.

Figure 3—source data 1

Genes and reference list for endothelial and mesenchymal genes indicated in the Figure 3B–F heat map.

(A) Reference list for endothelial and mesenchymal genes indicated in the Figure 3B (EXE vs. SED) heat map. (B) Reference list for endothelial and mesenchymal genes indicated in the Figure 3C (aged vs. young) heat map. (C) Reference list for endothelial and mesenchymal genes indicated in the Figure 3D (high-fat diet [HFD] vs. Chow) heat map. (D) Reference list for endothelial and mesenchymal genes indicated in the Figure 3E (transverse aortic constriction [TAC] [2] vs. Sham) heat map. (E) Reference list for endothelial and mesenchymal genes indicated in the Figure F (TAC [7] vs. Sham) heat map.

https://cdn.elifesciences.org/articles/62678/elife-62678-fig3-data1-v2.docx
Figure 3—source data 2

Source data for Figure 3B, C, D, E and F.

https://cdn.elifesciences.org/articles/62678/elife-62678-fig3-data2-v2.xlsx
Figure 3—figure supplement 1
Cardiovascular disease (CVD) risk factors induce inflammatory gene expression in cardiac endothelial cells (ECs).

(A–E) Expression of inflammatory genes in the cardiac ECs of the indicated experimental groups. Genes were identified by comparing our data set with the Gene ontology term: Inflammatory response (GO:0006954) described in the http://www.informatics.jax.org/vocab/gene_ontology/GO:0006954. In panel (A–E), the up- and downregulated genes with false discovery rate (FDR; Benjamini–Hochberg adjusted p-value) threshold of 0.05 were considered. In the heatmap, each color-coded circle (red, green, and black) indicates the gene expression data obtained from individual biological sample per experimental group. N = 3–4 male mice per group were analyzed.

Figure 3—figure supplement 2
Obesity and pressure overload induce SASP gene expression and senescence in the heart.

(A and B) Representative images and quantification of the SA-β-galactosidase staining (in blue) detected at pH 6.0 in the high-fat diet and chow diet fed mouse hearts. (C–E) Expression of senescence-associated secretory phenotype (SASP) genes in the cardiac endothelial cells (ECs) of the indicated experimental groups. Genes were identified by comparing our data set with the previously published data sets deposited in the following databases: SASP Atlas (http://www.saspatlas.com) and SenQuest (https://senequest.net), and the endothelial expression of the genes were verified using Tabula Muris. In panels C–E, the up- and downregulated genes with false discovery rate (FDR; Benjamini–Hochberg adjusted p-value) threshold of 0.05 were considered. In the heatmap, each color-coded circle (red, green, and black) indicates the gene expression data obtained from individual biological sample per experimental group. N = 3–4 male mice per group were analyzed. In panels A and B (N = 3–4 male mice per group), Scale bar 100 μm. Data is presented as mean ± SEM. Student’s t-test was used, *p<0.05, **p<0.01, ***p<0.001.

Figure 3—figure supplement 3
QPCR validation of selected genes in the cardiac endothelial cells (ECs) of aged, obese, and exercise trained mice.

(A–F) mRNA expression of Apln, Vim, Tgfbr2, Vash1, Sparc, and Tgfb1 in the cardiac EC of the indicated experimental groups (N = 4–6 male mice/group). Gene expression is normalized to Hprt1 expression. Data is presented as mean ± SEM. Student’s t-test was used, *p<0.05, **p<0.01, ***p<0.001.

Figure 4 with 3 supplements
Serpinh1 expression is increased by aging and obesity and repressed by exercise training.

(A) A Venn diagram showing the overlap of differentially expressed genes between the experiments. Four genes were identified to be significantly affected by aging, obesity, and exercise (Serpinh1, Vwa1, Mest, and Fhl3). (B) Bar plot showing the expression pattern of these four genes. In panels (A and B), the up- and downregulated genes with the false discovery rate (FDR; Benjamini–Hochberg adjusted p-value) threshold of 0.05 were considered to be significant (N = 3–4 male mice/group). (C) qPCR validation of Serpinh1 and Vwa1 normalized to Hprt1 (N = 4–6 male mice/group). (D) In silico secretome analysis of the identified genes. (E–G) Representative immunofluorescent and immunohistochemistry images showing the expression of SERPINH1 in human endothelial cell (EC) and human heart samples. Red arrowhead in the bottom panel F indicates the expression in large vessels and ‘L’ indicates vessel lumen. White arrowheads in the panel G denote the co-expression of SERPINH1 and CDH5 in coronary vessels (yellow signal). (H–K) mRNA expression of Serpinh1, Vwa1, Vim, and Tgfbr2 in the cardiac ECs of sedentary and exercise trained aged mice (N = 4–5 female mice/group). Scale bar 100 μm. Data is presented as mean ± SEM. Student’s t-test was used, *p<0.05, **p<0.01, ***p<0.001.

Figure 4—figure supplement 1
Expression of Serpinh1 in different cardiac cell types and in the human heart.

A tSNE plot showing (A) cardiac cell types in the adult mouse heart. (B) Violin plots showing the levels of SerpinH1 transcripts in fibroblasts, myofibroblasts, smooth muscle cells, endothelial cells (ECs), endocardial cells, cardiac muscle cells, and leucocytes (each black dot denotes a single cell). (C) tSNE plot showing the expression of Serpinh1 and EndMT genes in the EC cluster (cells within the red circle) and endocardial cluster (cells within the green circle). Illustrations in the panels A–C were analyzed and acquired from a publicly available single-cell database Tabula muris: https://tabula-muris.ds.czbiohub.org/. (D) Representative longitudinal IHC image of human heart demonstrating strong SERPINH1 expression in interstitial cells (fibroblasts, ECs) and weak staining within cardiomyocytes. Scale bar 100 μm.

Figure 4—figure supplement 2
Serpinh1 expression in different subsets of cardiac endothelial cell (EC).

(A–D) A t-SNE plot showing the blood EC (activated EC (Apln+) highlighted within red circle), lymphatic EC (lyve1+ Prox1+), and endocardial EC (Npr3+). (E) Pdgfrb expression in cardiac EC population. (F) Serpinh1 is expressed in all EC populations, and especially highly expressed in Apln+ activated EC (cell cluster within red circle), which also has increased expression of mesenchymal genes (Tagln2, Vim, Smtn, and Cd44). Data in this figure were obtained and analyzed from publicly available EC atlas (https://endotheliomics.shinyapps.io/ec_atlas/).

Figure 4—figure supplement 3
SERPINH1 RNA levels in the human arterial and venous endothelial cells.

RNA expression of SERPINH1 in human arterial and venous endothelial cells in the indicated organs organs. Data obtained by analysing the publicly available datasets (E-GEOD-43475 in the EndoDB database; https://endotheliomics.shinyapps.io/endodb/).

Figure 5 with 1 supplement
Overexpression of SERPINH1 modifies the endothelial cell (EC) phenotype and induces mesenchymal gene expression in human cardiac ECs.

(A) Representative phase-contrast images of live human cardiac arterial EC (HCAEC) transduced with LV-CTRL and LV-SERPINH1-Myc, and quantification of the aspect ratio (length to width ratio) of the cell. (B) Representative immunofluorescent images showing the expression of Myc-tagged SERPINH1 in green, F-Actin in gray, and CDH5/VE-Cadherin in red. The inset within the white box shows magnified view of VE-Cadherin junctions in HCAECs. (C) qPCR analysis of endothelial and mesenchymal markers in SERPINH1 overexpressing cells. (D) Western blot analysis and quantification of CDH5/VE-cadherin expression in the SERPINH1 overexpressing HCAECs (normalized to GAPDH). (E) Representative immunofluorescent images showing DAPI in blue, CDH5/VE-Cadherin in green, and α-smooth muscle actin (aSMA) in red. (F) qPCR analysis of SERPINH1 and EndMT markers in HCAECs stimulated with TGF-β1 (50 ng/ml) or H2O2 for 5 days. (G) Representative images and quantification of SA-β-gal+ senescent cells (in blue) normalized to total nuclei (%) in SERPINH1 overexpressing and control cells. (H) qPCR analysis of senescence-associated secretory phenotype (SASP) genes in HCAECs transduced with LV-CTRL and LV-SERPINH1-Myc. In panels A, C, D, F, G, and H, N = 3 biological replicates/group were analyzed. Scale bar 100 μm. Data is presented as mean ± SEM. Student’s t-test was used, *p<0.05, **p<0.01, ***p<0.001.

Figure 5—figure supplement 1
SERPINH1 overexpression in endothelial cells.

(A) Western blot analysis of cMyc expression in LV-SERPINH1-Myc-treated human umbilical venous EC (HUVEC; N = 3/group).

SERPINH1 silencing in human cardiac endothelial cell (EC) inhibits collagen production and EndMT.

(A) Representative phase contrast images of live human cardiac arterial ECs (HCAECs) transduced with LV-SCR and LV-shSERPINH1 (#1 and #2) and quantification of the aspect ratio (length to width ratio) of the cells 48 hr after transduction. (B) Representative CDH5/VE-Cadherin immunofluorescent images (green) showing the cell morphology and density after 10 days of SERPINH1 silencing. Collagen 1 staining is shown in red, and quantification of collagen 1 is shown in C. (D) qPCR analysis of SERPINH1 deletion levels using four independent constructs. (E) Representative immunofluorescent images showing TAGLN expression in the control and SERPINH1 silenced HCAECs treated with recombinant human TGF-β1 with and without H2O2 for 5 days. In the panels (A, C and D), N = 3 biological replicates/group were analyzed. Scale bar 100 μm. Data is presented as mean ± SEM. Student’s t-test was used, *p<0.05, **p<0.01, ***p<0.001.

SERPINH1 overexpression enhances and silencing inhibits wound closure in vitro.

(A and B) Representative phase contrast images of scratch wound healing assay performed in human cardiac arterial ECs (HCAECs) treated with LV-CTRL and LV-SERPINH1, and quantification of the wound closure (%) with respect to time (hours). (C and D) Representative phase contrast images of scratch wound healing assay performed in HCAECs treated with LV-SCR and LV-shSERPINH1 (#1 and #2), and quantification of the wound closure (%) with respect to time (hours). (E–G) Representative immunofluorescent images of EdU incorporation in HCAECs treated with LV-Ctrl, LV-SERPINH1-Myc, LV-Scr, and LV-shSERPINH1 (#1, #2, #3, and #4), and quantification of EdU+ nuclei (red) normalized to Hoechst+ nuclei (blue). In the panels (A and C), the blue area within the white dotted region indicates the wound area. In the panels (B and D), N = 8 biological replicates/group and in (F and G), N = 3 biological replicates/group were analyzed. Scale bar 100 μm. Data is presented as mean ± SEM. Student’s t-test was used, *p<0.05, **p<0.01, ***p<0.001.

Schematic demonstrating the cardiovascular disease risk factor mediated activation of TGF-β signaling and acquisition of mesenchymal gene features in cardiac EC.

CVD risk factors aging, obesity and pressure overload trigger the regression of coronary vasculature by activating TGF-β/ROS signaling pathways and cellular senescence. These induce the expression of SerpinH1/HSP47 and mesenchymal gene signature. SerpinH1/HSP47 and EndMT are both involved in the development of tissue fibrosis by increasing collagen deposition in the extracellular matrix. Exercise training, in turn, increases coronary vasculature density, EC number, and represses TGF-β signaling, mesenchymal gene expression, and cellular aging related pathways.

Tables

Key resources table
Reagent type
(species)
or resource
DesignationSource or
reference
IdentifiersAdditional
information
Genetic reagent (M. musculus)C57BL/6JJanvier LabsRRID:IMSR_JAX:000664
Cell line (Homo sapiens)HCAEC, HUVECPromoCell
Transfected construct (Homo sapiens)SERPINH1 (Homo sapiens), shRNA(#1)TRC library database, Broad instituteTRCN0000003590Lentiviral construct to transfect and express the shRNA
Transfected construct (Homo sapiens)SERPINH1 (Homo sapiens), shRNA(#2)TRC library database, Broad instituteTRCN0000003594Lentiviral construct to transfect and express the shRNA
Transfected construct (Homo sapiens)SERPINH1 (Homo sapiens), shRNA(#3)TRC library database, Broad instituteTRCN0000003593Lentiviral construct to transfect and express the shRNA
Transfected construct (Homo sapiens)SERPINH1 (Homo sapiens), shRNA(#4)TRC library database, Broad instituteTRCN0000003591Lentiviral construct to transfect and express the shRNA
Transfected construct (Homo sapiens)FUW-SERPIH1-Myc (Homo sapiens)This paperLentiviral construct to transfect and express overexpress SERPINH1-Myc
Biological sample (Homo sapiens)Heart samples from donorsHelsinki University Hospital
Antibody(Rat monoclonal), FITC-CD31InvitrogenRM5201, RRID:AB_10373983FACS (1:100)
Antibody(Rat monoclonal), Pacificblue-CD45Biolegend103125, RRID:AB_493536FACS (1:100)
Antibody(Rat monoclonal), Pacificblue-Ter119Biolegend116231, RRID:AB_2149212FACS (1:100)
Antibody(Rat monoclonal), PE-Cyanine7-CD140aeBioscience25-1401, RRID:AB_2573399FACS (1:100)
Antibody(Rat monoclonal), CD16/CD32 (Fc blocker)BD Biosciences553142, RRID:AB_394657FACS (1:100)
Antibody(Rat monoclonal), CD31BD Pharmingen553370, RRID:AB_394816Immunofluorescent (1:500)
Antibody(Rabbit monoclonal), VEcadherinCell Signaling2500S, RRID:AB_10839118Immunofluorescent or western blotting (1:500)
Antibody(Sheep Polyclonal), TaglnR&D BiosystemsAF7886Immunofluorescent (1:500)
Antibody(Mouse Monoclonal), c-MYCThermo Fisher13-2500, RRID:AB_2533008Immunofluorescent or western blotting (1:500)
Antibody(Mouse Monoclonal), HSP47/SERPINH1Enzo Life SciencesADI-SPA-470-D, RRID:AB_2039239Immunofluorescent immunohistochemistry or western blotting (1:1000)
Antibody(Rabbit Polyclonal), Collagen 1Abcamab34710, RRID:AB_731684Immunofluorescent (1:1000)
Antibody(Mouse Monoclonal), aSMASigma-AldrichA5228, RRID:AB_262054Immunofluorescent (1:500)
Antibody(Mouse
Monoclonal), GAPDH
MilliporeCB1001, RRID:AB_2107426Western blotting
(1:500)
Sequence-based reagenthSERPINH1_FThis paperSYBR green PCR primersATGAGAAATTCCACCACAAGATG
Sequence-based reagenthSERPINH1_RThis paperSYBR green PCR primersGATCTTCAGCTGCTCTTTGGTTA
Sequence-based reagenthCD31_FThis paperSYBR green PCR primersCTGCTGACCCTTCTGCTCTGTTC
Sequence-based reagenthCD31_RThis paperSYBR green PCR primersGGCAGGCTCTTCATGTCAACACT
Sequence-based reagenthCDH5_FThis paperSYBR green PCR primersCGTGAGCATCCAGGCAGTGGTAGC
Sequence-based reagenthCDH5_RThis paperSYBR green PCR primersGAGCCGCCGCCGCAGGAAG
Sequence-based reagenthTIE1_FThis paperSYBR green PCR primersACCCGCTGTGAACAGGCCTGCAGAGA
Sequence-based reagenthTIE1_RThis paperSYBR green PCR primersCTTGGCACTGGCTTCCTCT
Sequence-based reagenthCYCLIND1_FThis paperSYBR green PCR primersGCGGAGGAGAACAAACAGAT
Sequence-based reagenthCYCLIND1_RThis paperSYBR green PCR primersTGAGGCGGTAGTAGGACAGG
Sequence-based reagenthTAGLN_FThis paperSYBR green PCR primersCGGTTAGGCCAAGGCTCTAC
Sequence-based reagenthTAGLN_RThis paperSYBR green PCR primersCCAGCTCCTCGTCATACTTC
Sequence-based reagenthaSMA_FThis paperSYBR green PCR primersAAGCACAGAGCAAAAGAGGAAT
Sequence-based reagenthaSMA_RThis paperSYBR green PCR primersATGTCGTCCCAGTTGGTGAT
Sequence-based reagenthCD44_FThis paperSYBR green PCR primersTGGCACCCGCTATGTCGAG
Sequence-based reagenthCD44_RThis paperSYBR green PCR primersGTAGCAGGGATTCTGTCTG
Sequence-based reagenthVIM_FThis paperSYBR green PCR primersCGAGGAGAGCAGGATTTCTC
Sequence-based reagenthVIM_RThis paperSYBR green PCR primersGGTATCAACCAGAGGGAGTGA
Sequence-based reagenthNOTCH3_FThis paperSYBR green PCR primersACCGATGTCAACGAGTGTCT
Sequence-based reagenthNOTCH3_RThis paperSYBR green PCR primersGTTGACACAGGGGCTACTCT
Sequence-based reagenthZEB2_FThis paperSYBR green PCR primersGAGGCGCAAACAAGCCAATC
Sequence-based reagenthZEB2_RThis paperSYBR green PCR primersTCAGAACCTGTGTCCACTAC
Sequence-based reagenthSLUG_FThis paperSYBR green PCR primersACTCCGAAGCCAAATGACAA
Sequence-based reagenthSLUG_RThis paperSYBR green PCR primersCTCTCTCTGTGGGTGTGTGT
Sequence-based reagenthFN1_FThis paperSYBR green PCR primersCCATAGCTGAGAAGTGTTTTG
Sequence-based reagenthFN1_RThis paperSYBR green PCR primersCAAGTACAATCTACCATCATCC
Sequence-based reagenthVCAM1_FThis paperSYBR green PCR primersCGCAAACACTTTATGTCAATGTTG
Sequence-based reagenthVCAM1_RThis paperSYBR green PCR primersGATTTTCGGAGCAGGAAAGC
Sequence-based reagenthICAM1_FThis paperSYBR green PCR primersTGCCCTGATGGGCAGTCAAC
Sequence-based reagenthICAM1_RThis paperSYBR green PCR primersCCCGTTTCAGCTCCTTCTCC
Sequence-based reagenthHPRT1_FThis paperSYBR green PCR primersTGAGGATTTGGAAAGGGTGT
Sequence-based reagenthHPRT1_RThis paperSYBR green PCR primersTCCCCTGTTGACTGGTCATT
Sequence-based reagenthCDKN1A_FThis paperSYBR green PCR primersCAGCATGACAGATTTCTACC
Sequence-based reagenthCDKN1A_RThis paperSYBR green PCR primersCAGGGTATGTACATGAGGAG
Sequence-based reagenthCDKN2A_FThis paperSYBR green PCR primersAGCATGGAGCCTTCG
Sequence-based reagenthCDKN2A_RThis paperSYBR green PCR primersATCATGACCTGGATCGG
Sequence-based reagenthIL6_FThis paperSYBR green PCR primersGCAGAAAAAGGCAAAGAATC
Sequence-based reagenthIL6_RThis paperSYBR green PCR primersCTACATTTGCCGAAGAGC
Sequence-based reagenthIL7_FThis paperSYBR green PCR primersTCGATCATTATTGGACAGC
Sequence-based reagenthIL7_RThis paperSYBR green PCR primersAGGAAACACAAGTCATTCAG
Sequence-based reagenthMMP10_FThis paperSYBR green PCR primersACCAATTTATTCCTCGTTGC
Sequence-based reagenthMMP10_RThis paperSYBR green PCR primersGTCCGTAGAGAGACTGAATG
Sequence-based reagenthCXCL5_FThis paperSYBR green PCR primersATTTGTCTTGATCCAGAAGC
Sequence-based reagenthCXCL5_RThis paperSYBR green PCR primersTCAGTTTTCCTTGTTTCCAC
Sequence-based reagenthANKRD1_FThis paperSYBR green PCR primersTGAGTATAAACGGACAGCTC
Sequence-based reagenthANKRD1_RThis paperSYBR green PCR primersTATCACGGAATTCGATCTGG
Sequence-based reagenthPLAT_FThis paperSYBR green PCR primersGGAATTCCATGATCCTGATAG
Sequence-based reagenthPLAT_RThis paperSYBR green PCR primersTCCGGCAGTAATTATGTTTG
Sequence-based reagenthPAI-1_FThis paperSYBR green PCR primersCGCAACGTGGTTTTCTC
Sequence-based reagenthPAI-1_RThis paperSYBR green PCR primersCATGCCCTTGTCATCAATC
Sequence-based reagenthNRARPThis paperTaqman
PCR probes
Hs01104102_S1
Sequence-based reagentmCdh5This paperTaqman
PCR probes
Mm00486938_m1
Sequence-based reagentmTie1This paperTaqman
PCR probes
Mm00441786_m1
Sequence-based reagentmSerpinH1_FThis paperSYBR green PCR primersATGTTCTTTAAGCCACACTG
Sequence-based reagentmSerpinH1_RThis paperSYBR green PCR primersTCGTCATAGTAGTTGTACAGG
Sequence-based reagentmVwa1_FThis paperSYBR green PCR primersGATGATCTTCCTATCATTGCC
Sequence-based reagentmVwa1_RThis paperSYBR green PCR primersCAATTCCAGCACGTAGTAAC
Sequence-based reagentmVim_FThis paperSYBR green PCR primersCTTGAACGGAAAGTGGAATCCT
Sequence-based reagentmVim_RThis paperSYBR green PCR primersGTCAGGCTTGGAAACGTCC
Sequence-based reagentmTgfbr2_FThis paperSYBR green PCR primersTCTTTTCGGAAGAATACACC
Sequence-based reagentmTgfbr2_RThis paperSYBR green PCR primersGTAGCAGTAGAAGATGATGATG
Sequence-based reagentmVash1_FThis paperSYBR green PCR primersCAAGGAAATGACCAAAGAGG
Sequence-based reagentmVash1_RThis paperSYBR green PCR primersACTGTTGGTGAGGTAAATTC
Sequence-based reagentmSparc_FThis paperSYBR green PCR primersGAACCCACATGGCAAGTCTTA
Sequence-based reagentmSparc_RThis paperSYBR green PCR primersAAAGCCCAATTGCAGTTGAGT
Sequence-based reagentmTgfb1_FThis paperSYBR green PCR primersCTCCCGTGGCTTCTAGTGC
Sequence-based reagentmTgfb1_RThis paperSYBR green PCR primersGCCTTAGTTTGGACAGGATCTG
Sequence-based reagentmApln_FThis paperSYBR green PCR primersCAGGCCTATTCCCAGGCTCA
Sequence-based reagentmApln_RThis paperSYBR green PCR primersCAAGATCAAGGGCGCAGTCA
Peptide, recombinant proteinRecominant human TGF- βR&D Technologies240-B50 ng/ml
Commercial assay or kitHigh-Capacity cDNA Reverse Transcription KitApplied biosystems#4368814
Commercial assay or kitFastStart Universal SYBR green master mixSigma-Aldrich#04913914001
Commercial assay or kitTaqMan gene expression master mixApplied Biosystems#4369016
Commercial assay or kitSMARTer Stranded Total RNA-Seq Kit V2 – Pico Input MammalianTakara Bio, USA
Commercial assay or kitSA-β-gal staining kitCell signaling technology#9860
Commercial assay or kitClick-iT EdU Alexa Fluor 594 staining kitThermo scientificC10339
Chemical compound, drugHydrogen peroxideAcros organicsAC202465000200 μM
Software, algorithmChipster analysis platform (v3.12.2)CSC, Finlandhttps://chipster.csc.fi
Software, algorithmTrimmomatic toolChipster, CSC, Finlandhttps://chipster.csc.fi/manual/trimmomatic.html
Software, algorithmHISAT2 packageChipster, CSC, Finlandhttps://chipster.csc.fi/manual/hisat2.html
Software, algorithmHTSeq count toolChipster, CSC, Finlandhttps://chipster.csc.fi/manual/htseq-count.html
Software, algorithmDESeq2 Bioconductor packageChipster, CSC, Finlandhttps://chipster.csc.fi/manual/deseq2-pca-heatmap.html
Software, algorithmPANTHER classification system (V.14.1)http://www.pantherdb.org
Software, algorithmVENNY 2.1 Venn-diagram analysisBioinfoGPhttps://bioinfogp.cnb.csic.es/tools/venny/
Software, algorithmMetazSecKB knowledgebasehttp://proteomics.ysu.edu/secretomes/animal/index.php
Software, algorithmTargetP2.0 serverhttp://www.cbs.dtu.dk/services/TargetP/index.php
Software, algorithmSecretomeP1.0 serverhttp://www.cbs.dtu.dk/services/SecretomeP-1.0/
Software, algorithmImage J softwareNIH, Bethesdahttps://imagej.nih.gov/ij/download.html
Software, algorithmSASP atlashttp://www.saspatlas.com
Software, algorithmSeneQuesthttps://senequest.net
Software, algorithmTabula Murishttps://tabula-muris.ds.czbiohub.org
Software, algorithmEndoDBhttps://endotheliomics.shinyapps.io/endodb/
Software, algorithmEndothelial cell atlashttps://endotheliomics.shinyapps.io/ec_atlas/

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