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ETS1 governs pathological tissue-remodeling programs in disease-associated fibroblasts

Nature Immunology volume 23, pages 1330–1341 (2022)Cite this article

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Abstract

Fibroblasts, the most abundant structural cells, exert homeostatic functions but also drive disease pathogenesis. Single-cell technologies have illuminated the shared characteristics of pathogenic fibroblasts in multiple diseases including autoimmune arthritis, cancer and inflammatory colitis. However, the molecular mechanisms underlying the disease-associated fibroblast phenotypes remain largely unclear. Here, we identify ETS1 as the key transcription factor governing the pathological tissue-remodeling programs in fibroblasts. In arthritis, ETS1 drives polarization toward tissue-destructive fibroblasts by orchestrating hitherto undescribed regulatory elements of the osteoclast differentiation factor receptor activator of nuclear factor-κB ligand (RANKL) as well as matrix metalloproteinases. Fibroblast-specific ETS1 deletion resulted in ameliorated bone and cartilage damage under arthritic conditions without affecting the inflammation level. Cross-tissue fibroblast single-cell data analyses and genetic loss-of-function experiments lent support to the notion that ETS1 defines the perturbation-specific fibroblasts shared among various disease settings. These findings provide a mechanistic basis for pathogenic fibroblast polarization and have important therapeutic implications.

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Fig. 1: A distal enhancer element regulates RANKL gene expression in arthritic synovial fibroblasts.
Fig. 2: E3 deletion ameliorates arthritis-induced bone damage.
Fig. 3: Transcriptional regulation of tissue-destructive genes by ETS1.
Fig. 4: Ets1 deletion in synovial fibroblasts attenuates arthritic joint damage.
Fig. 5: Transcriptome analysis of Ets1-deleted fibroblasts.
Fig. 6: Cross-tissue analysis of fibroblast single-cell datasets reveals the ETS1 expression is associated with perturbation-specific fibroblast phenotypes.
Fig. 7: Ets1-expressing fibroblasts contribute to tissue remodeling and healing upon epithelial injury.

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Data availability

All the data that support the plots within this paper are available in the main text or in the Supplementary Information. The scRNA-seq data and bulk RNA-seq data produced in this study are available in the public GEO database under accession codes GSE192504 and GSE201310. The referenced publicly available datasets were downloaded from the GEO, BioProject NCBI database, ENCODE Project database, TCGA database, National Bioscience Database Center (NBDC) database and MSigDB Collections C3 datasets: regulatory target gene sets (legacy transcription factor targets, https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). scRNA-seq (dbGaP study accession phs001529.v1.p1, ImmPort SDY998, GSE178341, SCP259, GSE176078, GSE136103, GSE135893, GSE140023 and GSE172261), ChIP–seq (GSE128642, GSE148399, GSE54782, GSE123198, GSE145951 and GSE155882; ENCODE: ENCFF614VLL, ENCFF982OBR, ENCFF577JGN, ENCFF169ARY, ENCFF244ZNY, ENCFF569PTY, ENCFF523ZCW, ENCFF213MVM, ENCFF383CFQ and ENCFF314JXH), ATAC–seq (GSE128644 and BioProject PRJNA643827), Hi-C (NBDC hum0207), bulk RNA-seq (GSE129451 and GSE148395; NBDC hum0207; GSE166927 and GSE166925), TCGA clinical data and sequencing data (pancreatic adenocarcinoma (PanCancer Atlas), adult soft tissue sarcomas, mesothelioma (PanCancer Atlas), lung adenocarcinoma (Firehose Legacy) and colon cancer CPTAC-2 prospective and colorectal adenocarcinoma). Source data are provided with this paper.

Code availability

R scripts for data analysis used in this study are available at GitHub (https://github.com/MingluYAN).

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Acknowledgements

We thank A. Terashima, T. Asano, T. Sugita, H. Urabe, S. Yin, W. Yin, S. Zang, K. Kusubata, A. Suematsu, K. Kubo, Y. Omata, N. Kamiya, M. Hatakeyama, M. Miyoshi, G. Zhao, K. Ohmura, J. Nishio and K. Nakano for thoughtful discussion and valuable technical assistance. We appreciate the great contribution of K. Onimaru for the data analysis. This work was supported in part by the Japan Agency for Medical Research and Development (AMED) under grant number JP22ek0410073, AMED-CREST under grant number JP22gm1210008 and AMED-PRIME under grant number JP22gm6310029h0001; Japan Society for the Promotion of Science (JSPS): Grants-in-Aid for Specially Promoted Research (15H05703), Scientific Research S (21H05046), Scientific Research B (21H03104 and 22H02844) and Challenging Research (20K21515 and 21K18254). M.Y. was supported by a JSPS Research Fellowship for Young Scientists (19J21942) and a JSPS Postdoctoral Fellowships for Overseas Researchers (22F22108).

Author information

Authors and Affiliations

  1. Department of Immunology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan

    Minglu Yan, Noriko Komatsu, Ryunosuke Muro, Nam Cong-Nhat Huynh, Hiroyuki Takaba, Warunee Pluemsakunthai, Takeshi Nitta, Masayuki Tsukasaki & Hiroshi Takayanagi

  2. Laboratory of Oral-Maxillofacial Biology, Faculty of Odonto-Stomatology, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Nam Cong-Nhat Huynh

  3. Department of Statistical Genetics, Osaka University, Graduate School of Medicine, Osaka, Japan

    Yoshihiko Tomofuji & Yukinori Okada

  4. Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Yukinori Okada

  5. Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan

    Yukinori Okada

  6. Division of Molecular Oncology, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, Nagoya, Japan

    Hiroshi I. Suzuki

  7. Institute for Glyco-core Research (iGCORE), Nagoya University, Nagoya, Japan

    Hiroshi I. Suzuki

  8. Division of Diagnostic Pathology, Ehime University Hospital, Toon City, Japan

    Riko Kitazawa

  9. Department of Molecular Pathology, Graduate School of Medicine, Ehime University, Toon City, Japan

    Sohei Kitazawa

  10. Department of Immune Regulation, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan

    Yuichi Mitsui & Takashi Satoh

  11. Innate Cell Therapy, Osaka, Japan

    Yuichi Mitsui & Takashi Satoh

  12. Department of Laboratory Animal Medicine, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan

    Tadashi Okamura

  13. Department of Life Sciences, Pohang University of Science and Technology (POSTECH), POSTECH Biotech Center, Pohang, Republic of Korea

    Sin-Hyeog Im & Chan Johng Kim

  14. ImmunoBiome, Pohang, Republic of Korea

    Sin-Hyeog Im

  15. Institute of Convergence Science, Yonsei University, Seoul, Republic of Korea

    Sin-Hyeog Im

  16. Institute for Bioinnovation, Biomedical Sciences Research Center (BSRC) ‘Alexander Fleming,’ Vari, Attika, Greece

    George Kollias

  17. Department of Physiology, Medical School, National and Kapodistrian University of Athens, Athens, Greece

    George Kollias

  18. Department of Orthopaedic Surgery, Faculty of Medicine, The University of Tokyo, Tokyo, Japan

    Sakae Tanaka

  19. Department of Osteoimmunology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan

    Kazuo Okamoto

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  1. Minglu Yan
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  2. Noriko Komatsu
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Contributions

M.Y. conceived the project and performed most of the experiments and data analyses, interpreted the results and wrote the manuscript. N.K., Y.O., H.I.S., H. Takaba, W.P. and T.N. provided advice on project planning and contributed to data interpretation and manuscript preparation. R.M. constructed the enhancer plasmids, performed reporter assay experiment and contributed to data interpretation. N.C.-N.H. and Y.T. performed computational analyses and contributed to data interpretation. R.K. and S.K. constructed and provided RANKL promoter luciferase plasmid and provided advice on project planning and data interpretation. Y.M., T.S. induced fibrosis model, performed the histological analysis and provided advice on data interpretation. T.O., S.-H.I., C.J.K. contributed to the generation of genetically modified mice and provided advice on data interpretation. G.K. generated and provided the Col6a1-Cre mice, advised on epigenomic data analysis and provided a thoughtful discussion on data interpretation. S.T. advised on project planning, provided the human synovium specimens and a thoughtful discussion on data interpretation. K.O. provided advice on project planning, immunostaining and histological analysis, data interpretation and manuscript preparation. M.T. supervised project planning and data interpretation and wrote the manuscript. H. Takayanagi directed the project and wrote the manuscript.

Corresponding author

Correspondence to Hiroshi Takayanagi.

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Competing interests

The Department of Osteoimmunology is an endowed department supported by unrestricted grants from AYUMI Pharmaceutical, Chugai Pharmaceutical, MIKI HOUSE and Noevir. S.-H.I. is the CEO of the ImmunoBiome but declares no competing interests for this paper. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Christopher Buckley and Caroline Ospelt for their contribution to the peer review of this work. Primary Handling Editor: L. A. Dempsey, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 A distal enhancer regulates RANKL gene expression in arthritic SFs.

a, The expression pattern of representative cell-type marker genes in the CIA synovial cells (n = 2,160) in the UMAP visualization. The feature plots of the expression distribution of the major cell-type marker genes corresponding to the assigned cell types shown in (Fig. 1a). b, c, UMAP plots and unsupervised clustering of the synovial cells of individuals with RA (b) dbGaP Study Accession: phs001529.v1.p1, n = 19,599 cells; (c) ImmPort Accession: SDY998, n = 9,553 cells) colored by cluster. The violin plots on the right sides show the expression of the known SF marker genes. d, UMAP plot of the integrated SFs (n = 11,188 cells) from RA. Eight clusters (F1-F8) identified through unsupervised graph-based clustering are shown in color. Violin plots show the expression of selected genes in the SF sub-clusters. e, The sequence similarity of E1-E5 between humans and other vertebrates analyzed by the ECR Browser. The homologous sequence regions of E1-E5 in the mouse genome are indicated by arrows. f, Epigenomic analyses at the Tnfsf11 locus (mm9) in osteoblasts (vitamin D3 stimulation followed by H3K27ac ChIP–seq and vitamin D receptor (VDR) ChIP–seq (GSE54782)), in T cells (anti-CD3/CD28 antibody stimulation followed by H3K27ac ChIP–seq (GSE123198)), in B cells (IL-2/IL-4/IL-5/CD40L stimulation followed by H3K27ac ChIP–seq (GSE145951)), in cardiac fibroblasts (TGFβ stimulation followed by H3K27ac ChIP–seq (GSE155882)), in arthritic SFs (TNF stimulation followed by ATAC–seq, PRJNA643827)). g, h, The expression levels of Tnfsf11 mRNA in arthritic SFs (WT: control, n = 3; stimulation, n = 2; E3-KO: control: n = 2; stimulation, n = 2) and lymphocytes (WT: control, n = 2; stimulation, n = 3; E3-KO: control: n = 2; stimulation, n = 4). Data were expressed as mean ± s.e.m. P values were determined by two-way ANOVA analysis followed by Turkey’s post hoc test.

Source data

Extended Data Fig. 2 E3 deletion ameliorates arthritis-induced bone damage.

a, Flow cytometry gating strategies and a representative FACS plot of synovial cells from WT and E3-KO AIA (day 7 after intra-articular injection) mice, the number shows a frequency of indicated population from more than three independent experiments. b, Flow cytometry gating strategies and a representative FACS plot of synovial cells from WT and E3-KO STIA (day 10) mice (n = 6-10), data were expressed as mean ± s.e.m. c, Synovial cell number per inflamed joint (WT, n = 6; E3-KO, n = 8) and the frequencies of CD45+ cells (WT, n = 6; E3-KO, n = 8), endothelial cells (WT, n = 6; E3-KO, n = 9), mural cells (WT, n = 6; E3-KO, n = 10), SFs (WT, n = 6; E3-KO, n = 10). Data were expressed as mean ± s.e.m. P values were determined by two-tailed t-test.

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Extended Data Fig. 3 ETS1 regulates the expression of tissue-destructive genes in SFs.

a, Motif enrichment analysis in RANKL enhancers by HOMER software (P values: hypergeometric test) b, The expression levels of ETS family members in RA patients-derived SFs under the indicated conditions (NBDC accession code hum0207). Data were expressed as mean ± s.e.m. P values were determined by one-way ANOVA analysis followed by Turkey’s post hoc test. c, d, The expression levels of ETS1 in RA patient-derived SF sub-clusters F1 to F8 (c) and CIA SFs sub-clusters mFib1 to mFib4 (d). e, Regulatory target gene sets analysis in CIA SFs sub-clusters using MSigDB C3 datasets (legacy transcription factor targets) performed with VISION R package (P values: Wilcoxon rank-sum test). f, Alignment of the ETS1 variants associated with RA. The upper panel shows the P-values of the 29 variants within the ETS1 region (Chr. 11: 127,828,656-128,957,453 in hg19) which exceeded the genome-wide significance threshold (P < 5 × 10-8) in a previously performed RA GWAS study27. The dashed line indicates the genome-wide significance threshold (P < 5 × 10-8). The lower panel (from top to bottom) shows the H3K27ac ChIP–seq (GSE128642) peaks detected in RA patient SFs, candidate cis-regulatory element (cCRE) with promoter-like signatures (cCRE_PLS), cCRE with proximal enhancer-like signatures (cCRE_pELS), cCRE with distal enhancer-like signatures (cCRE_dELS), cCRE enriched with DNase and H3K4me3 signals (cCRE_DNase_H3K4me3) and cCRE enriched with CTCF signals (cCRE_CTCF). Overlapping of the RA-associated ETS1 variants with the H3K27ac ChIP–seq (GSE128642) peaks detected in RA patient SFs and the candidate cCRE are indicated. The red-colored dots (ChIP–seq) and blue-colored dots (cCRE) represent overlaps. The shaded area indicates the overlap among the RA-associated ETS1 variants, and the H3K27ac peaks detected in RA patient SFs and cCRE.

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Extended Data Fig. 4 ETS1 deletion in SFs attenuates arthritis-induced joint damage.

a, The expression of Col6a1 in the CIA synovial cells (n = 2,160) in the UMAP visualization. b, Violin plots showing the expression of Thy1 and Col6a1 in the SF sub-clusters. c, The expression levels of Ets1 mRNA in sorted SFs (Ets1flox, n = 8; Ets1ΔFib, n = 6), leukocytes (Ets1flox, n = 4; Ets1ΔFib, n = 3) and endothelial cells (Ets1flox, n = 6; Ets1ΔFib, n = 5) of the inflamed synovium. Data were expressed as mean ± s.e.m. P values were determined by two-tailed t-test. d, e, Parameters of the micro-CT analysis (bone volume per tissue volume, trabecular number, trabecular thickness and trabecular spacing) of the femurs of Ets1flox and Ets1ΔFib mice (female, n = 4 per group) at the age of 8 weeks under physiological conditions. Data were expressed as mean ± s.e.m. P values were determined by two-tailed t-test (d). Representative micro-CT images (female, 8 weeks). Micro-CT scale bars: 1 mm (e). f, g, Representative FACS plot and gating strategies of STIA (day 16) synovial cells from Ets1flox and Ets1ΔFib mice, n = 6-12 (f). Synovial cell number per inflamed joint and the frequencies of CD3+ cells (Ets1flox, n = 9; Ets1ΔFib, n = 6), and the frequencies of the other cell types in synovial tissue of Ets1flox mice (n = 12) and Ets1ΔFib STIA mice (n = 8). Data were expressed as mean ± s.e.m. P values were determined by two-tailed t-test (g).

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Extended Data Fig. 5 ETS1+ SFs increased in number in the destruction phase during CIA.

a, Representative FACS plot of SFs derived from the mice of untreated control and CIA (inflammation phase: day 28; destruction phase: day 42 to day 50). b, Quantification of the cell number of ETS1+ fibroblasts of n = 5 mice in inflammation phase, n = 6 mice in destruction phase and NOTCH3 + fibroblasts of n = 6 mice in inflammation phase, n = 6 mice in destruction phase in the joints. Data were expressed as mean ± s.e.m. P values were determined by two-tailed t-test.

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Extended Data Fig. 6 RNA-seq analysis of Ets1-deleted fibroblasts.

a, Representative FACS plot of SFs (STIA day 16) from Ets1flox and Ets1ΔFib mice, n = 3. b, Three-dimensional PCA analysis for the top 2500 variable genes in SFs derived from Ets1flox and Ets1ΔFib mice, n = 3. c, Heat map showing the DEGs between Ets1flox- and Ets1ΔFib-SFs. (DEGs: log2 FC > 0.5, P < 0.05 determined by DESeq2 (Wald test with Benjamini and Hochberg method correction)).

Extended Data Fig. 7 Cross-tissue analysis of fibroblast scRNA-seq datasets.

a, b, Mouse perturbed-state fibroblasts atlas4 and the expression of Ets1, Tnfsf11, Mmp13 and Mmp3 in the identified clusters (a) colored by Adamdec1 logcounts and in the fibroblast cells across diseases (b) (https://www.fibroxplorer.com/home). c, Dot plot showing the expression of ETS1, TNFSF11, MMP3 and MMP13 in ulcerative colitis (UC)-associated stromal cells and healthy controls (SCP259). d, Dot plot showing the expression of ETS1, TNFSF11, MMP3 and MMP13 in stromal cells of tumor tissues and adjacent normal colon tissues of CRC patients (GSE178341). e, Dot plot showing the expression of ETS1, TNFSF11, MMP3 and MMP13 across CRC-associated fibroblast clusters (GSE178341). f, Expression levels of TNFSF11, MMP13, MMP3 in TCGA datasets of CRC patients. Groups were split by the expression levels of ETS1 (top third ETS1high, n = 84; bottom third ETS1low, n = 84). Box plots (center: median; limits: upper 75th percentile, lower: 25th percentile); whiskers were drawn independently above and below the box and extended to the maximum and minimum values (1.5*interquartile range, points were outliers). P values were calculated by two-tailed Student’s t-test.

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Extended Data Fig. 8 Ets1-expressing fibroblasts contribute to mucosal remodeling in colitis.

a, UMAP embedding of 34,197 mesenchymal cells derived from colon tissues (GSE172261) of DSS-fed mice colored by cluster. SMCs: smooth muscle cells, BECs: blood endothelial cells, LECs: lymphatic endothelial cells, ICCs: interstitial cells of Cajal, MSCs: mesenchymal stem cells. b, Dot plot showing the expression of representative cell-type marker genes for cell type annotation in (a). c, Representative immunofluorescence images of colons derived from Ets1flox and Ets1ΔFib mice after DSS administration (day 10), n = 3. Scale bar: 100 μm.

Extended Data Fig. 9 Hypoxia may contribute to the ETS1 regulation in fibroblasts.

a, Chromatin conformation and epigenomic analyses of SFs from RA patients at the ETS1 locus (hg19) by Hi-C and H3K4me3, H3K27ac and ATAC-seq (NBDC accession code hum0207 and GSE128644). A loop-like structure appears between the ETS1 promoter (open chromatin region with H3K4me3 signals) and enhancer region (open chromatin region with H3K27ac signals) under the condition (TNF stimulation) that could stimulate ETS1 expression in both synovial and intestinal fibroblasts (shown in (b)). The identified binding motif for hypoxia-inducible factor (HIF) (Jaspar, MA0259.1) was shown below. NS: no stimulation. b, The expression levels of ETS1 in SFs (NS, n = 28; TNF, n = 29) and intestinal fibroblasts (n = 6 per group) (NBDC accession code hum0207 and GSE166927). Data were expressed as mean ± s.e.m. P values were calculated by two-tailed t-test. c, The expression levels of EPAS1 (NS, n = 28; TNF, n = 29) and HIF1A in SFs (NS, n = 28; TNF, n = 29) (NBDC accession code hum020724). Data were expressed as mean ± s.e.m. P values were calculated by two-tailed t-test. d, Representative immunofluorescence images of RA synovium and inflamed colons, n = 2-3. Scale bars: 100 μm. e, Correlation analysis (Pearson r and two-tailed P value) between ETS1 expression and HIF2 (encoded by EPAS1) expression in arthritic SFs (n = 27) (GSE129451) and in tissues of colorectal cancer (CRC, n = 42), Crohn’s disease (CD, n = 34) and ulcerative colitis (UC, n = 13) patients (GSE166925).

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Extended Data Fig. 10 ETS1 and PU.1 expression in fibroblasts of fibrosis and arthritis.

a, UMAP embedding of 6,898 cells derived from cirrhotic human livers (GSE136103) colored by cluster. b, Feature plot showing the expression of representative cell-type marker genes for cell type annotation in (a). c, d, The expression of representative genes (MYH11, RGS5, LUM, COL3A1, PDGFRA, COL1A1) (c) and ETS1 (colored by red), SPI1 (colored by blue) (d) in the identified mesenchymal cells (cluster 10 and cluster 13) of liver cirrhosis in the UMAP visualization. e, UMAP embedding of 119,438 cells derived from pulmonary fibrosis (GSE135893) colored by cell type. f, Feature plot showing the expression of representative cell-type marker genes for cell type annotation in (e). g, Unsupervised graph-based clustering of fibroblasts revealed four populations colored by cluster and the expression of selected genes in the UMAP visualization. h, The expression of ETS1 (colored by red) and SPI1 (colored by blue) in the fibroblasts of pulmonary fibrosis. i, j, A correlation between ETS1 and SPI1 expression in fibroblasts of liver cirrhosis (i) and pulmonary fibrosis (j). Each dot represents a single cell of the annotated clusters. k, UMAP embedding of 16,370 cells of unilateral-ureteric-obstruction (UUO)-induced kidney fibrosis (GSE140023) colored by cluster. l, Violin plots showing the expression of representative cell-type marker genes for cell type annotation in (k). m, Unsupervised graph-based clustering of fibroblasts revealed three populations colored by cluster and the expression of selected genes in the UMAP visualization. n, The expression of Ets1 (colored by red) and Spi1 (colored by blue) in the fibroblasts of kidney fibrosis. o, A correlation between Ets1 and Spi1 expression in fibroblasts of kidney fibrosis. Each dot represents a single cell of the annotated clusters. p, q, Representative H&E stained, Azan stained and immunofluorescence images of lung tissues of untreated, bleomycin-induced lung fibrosis model in mice (p) and RA synovium (q), n = 2-3. Scale bars: 100 μm.

Supplementary information

Reporting Summary

Supplementary Table 1

Sequences of oligonucleotides (sgRNAs, ssODNs and primers)

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Yan, M., Komatsu, N., Muro, R. et al. ETS1 governs pathological tissue-remodeling programs in disease-associated fibroblasts. Nat Immunol 23, 1330–1341 (2022). https://doi.org/10.1038/s41590-022-01285-0

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