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Chemotherapy-induced transposable elements activate MDA5 to enhance haematopoietic regeneration

Abstract

Haematopoietic stem cells (HSCs) are normally quiescent, but have evolved mechanisms to respond to stress. Here, we evaluate haematopoietic regeneration induced by chemotherapy. We detect robust chromatin reorganization followed by increased transcription of transposable elements (TEs) during early recovery. TE transcripts bind to and activate the innate immune receptor melanoma differentiation-associated protein 5 (MDA5) that generates an inflammatory response that is necessary for HSCs to exit quiescence. HSCs that lack MDA5 exhibit an impaired inflammatory response after chemotherapy and retain their quiescence, with consequent better long-term repopulation capacity. We show that the overexpression of ERV and LINE superfamily TE copies in wild-type HSCs, but not in Mda5−/− HSCs, results in their cycling. By contrast, after knockdown of LINE1 family copies, HSCs retain their quiescence. Our results show that TE transcripts act as ligands that activate MDA5 during haematopoietic regeneration, thereby enabling HSCs to mount an inflammatory response necessary for their exit from quiescence.

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Fig. 1: 5-FU treatment results in the upregulation of inflammatory signalling in HSCs.
Fig. 2: Rapid TE upregulation in HSCs after 5-FU treatment.
Fig. 3: MDA5 is required for HSC activation.
Fig. 4: TE upregulation in Mda5−/− HSCs after chemotherapy.
Fig. 5: Impaired inflammatory signalling in Mda5−/− HSCs.
Fig. 6: 5-FU-induced inflammation is MDA5-dependent.
Fig. 7: Intrinsic role of Mda5 in HSCs.
Fig. 8: TE overexpression leads to HSC activation and knockdown leads to quiescence.

Data availability

Sequencing data supporting the findings of this study have been deposited at the Sequence Read Archive (SRA) under accession codes PRJNA532318 (FLASH data), PRJNA717283 (RNA-seq and ATAC-seq data) and PRJNA730379 (Setdb1 RNA-seq data). Single-cell RNA-seq data have been deposited at the Gene Expression Omnibus (GEO) under accession code GSE129631. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

All codes used in this manuscript are available from the corresponding author on reasonable request.

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Acknowledgements

We thank the staff at the deep-sequencing, imaging, FACS, mouse and bioinformatics facilities of the MPI-IE; D. Ryan for discussions on TE analysis; M. Derecka for reading, advising and helping with overnight experiments; and A. Choudhuri, A. Karoutas, R. Grosschedl, T. Bowman and L. Zon for reading the manuscript. Funding: Max Planck Society (to E.T., N.C.-W., R.S. and D.G.), The Fritz Thyssen Stiftung (Az 10.17.1.026MN, to E.T.), the German Research Foundation (DFG) (Research Training Group 322977937/GRK2344, to E.T. and D.G.; GZ: TR 1478/2-1, to E.T.; SPP1937 GR4980/1-1, to D.G.; GR4980/3-1, to D.G.), the DFG under Germany’s Excellence Strategy (CIBSS-EXC-2189-Project ID 390939984, to E.T., D.G., N.C.-W. and R.S.), the ERC (818846—ImmuNiche—ERC-2018-COG, to D.G., 759206-VitASTEM-ERC-Stg-2017 to N.C.-W., 819753-ChaperoneRegulome-ERC-2018-COG, to R.S.), and the Behrens–Weise Foundation (to D.G.and N.C.-W.). J.R and R.S. acknowledge funding by the UK Medical Research Council (MRC core funding of the MRC Human Immunology Unit and the Toxicology Unit, respectively).

Author information

Affiliations

Authors

Contributions

E.T. conceived the project. T.C. designed and performed experiments. A.P. performed the majority of computational analysis. P.P. designed and performed the FLASH experiments. P.P., L.K., N.K. S.L. and V.B. performed experiments. N.O. and N.C.-W. prepared the HSC RNA-seq libraries and provided advice. B.H. and D.M. performed analysis of the FLASH experiments. I.I. provided the protocol for the FLASH experiments and advice. R.B., R.S. and A.A. provided useful insights for the FLASH experiment and analysis. S. and D.G. performed and analysed the single-cell RNA-seq experiments. J.S.H. provided the code for analysis of TEs in the single-cell RNA-seq data. A.B. and J.R. provided the Mavs−/− and Sting/− bones. R.R. and M.G.F. provided useful advice and performed part of the TE analysis. A.M.-H. and S.M.-F. performed the cell cycle experiment on the LINE1 knockdown. T.C and E.T. wrote the manuscript with input from all of the authors. All of the authors have read and approved this manuscript.

Corresponding author

Correspondence to Eirini Trompouki.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature Cell Biology thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 HSC isolation after chemotherapy.

a, Schematic of the experimental strategy followed for the RNA-seq and ATAC-seq experiments on WT HSCs. b, Gating strategies for sorting HSCs from the BM of D0 or 5-FU-injected (H2, H6, H16, D3, D10) mice (15 biologically independent samples- representative plots are shown). c, Comparison of our sorting strategy (LSK/SLAM) to the HSCs sorted using EPCR/SLAM (EPCR+CD48CD1450+) markers. The EPCR/SLAM HSCs are then projected on the LSK/SLAM gating strategy (red color) and the percentage of EPCR/SLAM HSCs that are included in the LSK/SLAM gate is indicated (2 biologically independent samples- representative plots are shown). d, Comparison of the number of cells in the LSK/SLAM gate that are not EPCR/SLAM at D0 and H16 (2 biologically independent samples- representative plots are shown). e, Gene ontology analysis of the genes upregulated at H2, H16 and D10 after 5-FU injection compared to D0 in WT HSCs. X-axis depicts -logP. f, Venn diagrams depicting the overlap of differentially expressed genes (DEGs) in WT HSCs with genes assigned to newly accessible regions-gained ATAC peaks at the indicated time points compared to D0 (-100/+25 kb from TSS, p-values represent hypergeometric test). g, Gene ontology analysis of deregulated genes that also exhibit changes in chromatin accessibility at the indicated time points. X-axis depicts -logP.

Extended Data Fig. 2 TEs bind to MDA5 upon stress in human and mouse cells.

a, Bar graphs depicting the mean counts of LINE, SINE, LTR and DNA transposon (DNA) RNA (fold change >1.5 and p-value < 0.05) bound to MDA5 or GFP after irradiation or decitabine treatment (sense DNA strand-upper panel, antisense DNA strand-lower panel) (n = 2 biologically independent population samples, 2 independent experiments). b, Bar graphs depicting the mean counts of LINE, SINE, LTR and DNA transposon (DNA) RNA (fold change >1.5 and p-value < 0.05) bound to MDA5 after irradiation or decitabine treatment or to MDA5 without treatment (sense DNA strand-upper panel, antisense DNA strand-lower panel) (n = 2 biologically independent population samples, 2 independent experiments). c, Representative track that shows binding of L1M4c to GFP after irradiation or decitabine treatment and to MDA5 without treatment or after irradiation or decitabine treatment. Y-axis represents RPKM. Crosslinking events are also shown. d-e qPCR experiments after FLASH depicting binding of TEs to GFP or MDA5 after irradiation or decitabine treatment (d) or to MDA5 without treatment or after irradiation or decitabine treatment (e) (n = 2 biologically independent samples and experiments). f, qPCR experiment after FLASH from mouse OP9 cells depicting binding of LINE1 elements to GFP or MDA5 without treatment or after irradiation (n = 2 biologically independent samples and experiments).

Source data

Extended Data Fig. 3 MDA5 is required for HSC activation.

a, Representative profile comparing Sca-1 expression on the lineage negative fraction of the BM of WT or Mda5-/- mice (3 biologically independent samples, one representative plot is shown). b, Side population (SP) frequency in the BM of WT or Mda5-/- mice (n = 4 biologically independent samples, mean+s.d, two-tailed t-test, n.s. non-significant). c, Homing assay: percentage of donor derived LSK cells in the BM of WT recipients (n = 3 biologically independent samples) 16hrs after injection of BM cells from WT or Mda5-/- mice (mean + s.d., two-tailed t-test, n.s. non-significant). d, Percentage of donor derived myeloid, B or T lymphoid cells in the peripheral blood of recipients injected with BM cells isolated from WT (n = 30 biologically independent samples) or Mda5-/- mice (n = 27 biologically independent samples). Time (weeks) denotes the time after intravenous injection (mean ± s.d, two-tailed t-test, n.s. non-significant). e, Cell cycle analysis of HSCs and MPPs as indicated (n = 7 biologically independent samples, mean-s.d., two-tailed t-test). f, Bar graphs depicting the frequency of cells with detectable mitochondrial mass measured by MitoTracker Green (left panel) and reactive oxygen species (ROS) production (right panel) at D3 after 5-FU injection (n = 2 biologically independent samples and experiments). g, Images of γH2AX foci positive HSCs from WT or Mda5-/- mice (left) and quantification of γH2AX foci per nuclei at D3 after 5-FU injection (mean ± s.e.m., two-tailed t-test, n = 64-WT and 56-Mda5-/- cells examined in 2 independent experiments). h, Dot plot representing quantification of γH2AX foci per WT or Mda5-/- HSCs nuclei quantified with Imaris software 9.2 after culturing cells for 48 h (n = 121-WT and n = 127-Mda5-/- cells examined in 2 independent experiments, mean±s.e.m., two-tailed t-test, *P = 0.036). i, Cell cycle analysis of WT HSCs after cytarabine treatment (n = 4 biologically independent samples and experiments, mean-s.d., two-tailed t-test). j, Cell cycle analysis of WT and Mda5-/- HSCs after cyclophosphamide (n = 7 biologically independent samples and experiments, mean-s.d., two-tailed t-test).

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Extended Data Fig. 4 5-FU treatment in Mda5-/- HSCs.

a, Bar graphs depicting the number of differentially expressed genes at different time points after 5-FU treatment in Mda5-/-HSCs (H2, H16: n = 2 and D0, D3: n = 3 biologically independent samples, fold change cut-off 1.5, Padj < 0.05). b, Gene ontology of upregulated genes at indicated time points versus D0. c, t-SNE representation of Mda5-/- HSCs at D0 (red) and H16 (dark red) (left) (number of sequenced cells in parentheses). t-SNE representation of DEGs between H16 and D0 in Mda5-/- HSCs. Color scale: log2 of normalized transcript counts (right) d, Violin plots depicting log2 fold change expression at D0 or H16 in Mda5-/- HSCs. Box: interquartile range, whiskers: minimum and maximum values, horizontal line: median. Each dot represents a single cell; the plot shape declares probability density (n = 552 Mda5-/- D0 and n = 1096 H16 cells, one independent experiment per time point, Padj < 0.05). e, Gene set enrichment analysis in Mda5-/- HSCs from c between D0 and H16 f, t-SNE representation of WT (blue) and Mda5-/- HSCs (red) at D0 (number of sequenced cells in parentheses). g, t-SNE representation of DEGs between H16 and D0 in WT and Mda5-/- HSCs. Color scale: log2 of normalized transcript counts. h, Gene set enrichment analysis between WT and Mda5-/- HSCs at D0. i, t-SNE representation of WT (green) and Mda5-/- HSCs (red) at H16 (number of sequenced cells in parentheses). j, Violin plots depicting log2 expression of Cdk6 at H16 in WT and Mda5-/- HSCs. Box: interquartile range, whiskers: minimum and maximum values, horizontal line: median. Each dot represents a single cell; the plot shape declares probability density (n = 1087 WT H16 and n = 997 Mda5-/- H16 cells, one independent experiment per time point, Padj < 0.05). k, Venn diagrams depicting the overlap of differentially expressed genes (DEGs) with genes gaining accessibility (-100/+25 kb from TSS) (p-values: hypergeometric test). l, Table of upstream regulators for WT unique accessible regions assigned to proximal genes (+/-25kB) at H16.

Extended Data Fig. 5 5-FU treatment induces inflammation.

a, Representative plot depicting phospho-IRF3 staining in WT and Mda5-/- HSCs gated in LSK cells or HSCs (left panels). Tables depicting the mean fluorescence intensity (MFI) of pIRF3 in WT and Mda5-/- HSCs at the indicated time points (right panels) (two-tailed t-test, P values are indicated below). b, Measurement of secreted cytokines in WT (left) or Mda5-/- (right) BM at D0 and D3 (n = 7 D0 and n = 5 D3 for WT and n = 8 D0 and n = 5 D3 for Mda5-/- biologically independent samples, 2 independent experiments, two-tailed t-test, n.s: not significant, horizontal line: mean). c, Measurement of secreted cytokines in WT bone marrow at D0 (n = 4), H2 (n = 8), H6 (n = 6) and H16 (n = 6) (left) or D0 (n = 4) and D10 (n = 6) (right) (all biologically independent samples, 2 independent experiments two-tailed t-test, n.s: not significant, horizontal line: mean).

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Extended Data Fig. 6 TE expression affects HSC cell cycle.

a, Bar chart depicting the median fluorescence intensity (MFI) of γH2AX signal of WT or Mda5-/- HSCs 24 h after poly(I:C) injection (n = 5 biologically independent samples, mean±s.d., two-tailed t-test). b, RT-qPCR analysis of WT HSCs at D0, H2 and H16 after 5-FU injection for Setdb1 (n = 4 biologically independent samples for D0 and H2, n = 3 for H16 biologically independent samples in two independent experiments). c, Cell cycle analysis of WT HSCs after transfection of control or Setdb1 siRNA (n = 8 biologically independent samples for control siRNA and n = 9 for si-Setdb1 in two independent experiments, two-tailed t-test, mean + /-s.d). d, qRT-PCR analysis of HSCs 20 h after transfection with empty vector (EV), or both strands of the indicated TE copies in WT or Mda5-/- HSCs (n = 2 biologically independent samples). e, Cell cycle analysis of WT HSCs transfected with empty vector (EV) or the vector expressing GFP (n = 2 for EV and n = 3 for GFP biologically independent samples, two-tailed t-test, mean-s.d, n.s. non-significant). f, Measurement of secreted cytokines in supernatant of WT HSCs transfected as indicated (n = 8 biologically independent samples, 2 independent experiments, mean ± s.e.m, two-tailed t-test) g, qRT-PCR analysis of LSK cells 48 h or 72 h after knock-down of LINE1 families (n = 2 biologically independent samples). h, Cell cycle analysis of WT HSCs 48 hours after culture in presence of the indicated concentration of TBK1 inhibitor (BX795) (n = 2 biologically independent samples).

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Extended Data Fig. 7 Effect of the TE-MDA5-Inflammation axis on HSC activation.

Schematic showing that chromatin rearrangement occurs after chemotherapy concomitant to activation of TEs that are transcribed (H6-H16). TE transcripts bind to MDA5 to induce phosphorylation and thus activation of IRF3 and translocation of p65 to the nucleus (H16). This leads to activation of interferon responsive genes (H16) and secretion of proinflammatory cytokines (D3) followed by HSC cycling. Created with BioRender.com.

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Table 1. RNA-seq analysis of WT HSCs (LSK/SLAM). Differentially expressed genes H2 versus D0. Supplementary Table 2. RNA-seq analysis of WT HSCs (LSK/SLAM). Differentially expressed genes H6 versus D0. Supplementary Table 3. RNA-seq analysis of WT HSCs (LSK/SLAM). Differentially expressed genes H16 versus D0. Supplementary Table 4. RNA-seq analysis of WT HSCs (LSK/SLAM). Differentially expressed genes D3 versus D0. Supplementary Table 5. RNA-seq analysis of WT HSCs (LSK/SLAM). Differentially expressed genes D10 versus D0. Supplementary Table 6. Single−cell RNA-seq analysis of WT HSCs (LSK/SLAM). Differentially expressed genes H16 versus D0. Supplementary Table 7. ATAC-seq analysis of WT HSCs (LSK/SLAM). Gained peaks (100 kb upstream and 25 kb downstream from the TSS) H2 versus D0. Supplementary Table 8. ATAC-seq analysis of WT HSCs (LSK/SLAM). Gained peaks (100 kb upstream and 25 kb downstream from the TSS) H6 versus D0. Supplementary Table 9. ATAC-seq analysis of WT HSCs (LSK/SLAM). Gained peaks (100 kb upstream and 25 kb downstream from the TSS) H16 versus D0. Supplementary Table 10. ATAC-seq analysis of WT HSCs (LSK/SLAM). Gained peaks (100 kb upstream and 25 kb downstream from the TSS) D3 versus D0. Supplementary Table 11. ATAC-seq analysis of WT HSCs (LSK/SLAM). Gained peaks (100 kb upstream and 25 kb downstream from the TSS) D10 versus D0. Supplementary Table 12. RNA-seq analysis of WT HSCs (LSK/SLAM). Differentially expressed TEs. Supplementary Table 13. ATAC-seq analysis of WT HSCs (LSK/SLAM). Gained peaks for TEs in comparison to D0. Supplementary Table 14. Single-cell RNA-seq analysis of WT HSCs (LSK/SLAM). Differentially expressed TEs H16 versus D0. Supplementary Table 15. RNA-seq analysis of WT HSCs (LSK/SLAM). Differentially expressed TE copies. Supplementary Table 16. RNA binding to MDA5—FLASH experiment. All values. Supplementary Table 17. RNA binding to MDA5—FLASH experiment. Differentially bound TEs. Supplementary Table 18. RNA-seq analysis of Mda5-KO HSCs (LSK/SLAM). Differentially expressed TEs. Supplementary Table 19. Single-cell RNA-seq analysis of Mda5-KO HSCs (LSK/SLAM). Differentially expressed TEs H16 versus D0. Supplementary Table 20. RNA-seq analysis of Mda5-KO HSCs (LSK/SLAM). Differentially expressed TE copies. Supplementary Table 21. RNA-seq analysis of Mda5-KO HSCs (LSK/SLAM). Differentially expressed genes H2 versus D0. Supplementary Table 22. RNA-seq analysis of Mda5-KO HSCs (LSK/SLAM). Differentially expressed genes H16 versus D0. Supplementary Table 23. RNA-seq analysis of Mda5-KO HSCs (LSK/SLAM). Differentially expressed genes D3 versus D0. Supplementary Table 24. Single-cell RNA-seq analysis of Mda5-KO HSCs (LSK/SLAM). Differentially expressed genes H16 versus D0. Supplementary Table 25. Single-cell RNA-seq analysis of HSCs (LSK/SLAM). Differentially expressed genes D0 Mda5-KO versus D0 WT. Supplementary Table 26. Single-cell RNA-seq analysis of HSCs (LSK/SLAM). Differentially expressed genes H16 Mda5-KO versus H16 WT. Supplementary Table 27. ATAC-seq analysis of Mda5-KO HSCs (LSK/SLAM). Gained peaks (100 kb upstream and 25 kb downstream from the TSS) H2 versus D0. Supplementary Table 28. ATAC-seq analysis of Mda5-KO HSCs (LSK/SLAM). Gained peaks (100 kb upstream and 25 kb downstream from the TSS) H6 versus D0. Supplementary Table 29. ATAC-seq analysis of Mda5-KO HSCs (LSK/SLAM). Gained peaks (100 kb upstream and 25 kb downstream from TSS) H16 versus D0. Supplementary Table 30. ATAC-seq analysis of Mda5 KO HSCs (LSK/SLAM). Gained peaks (100 kb upstream and 25 kb downstream from TSS) D3 versus D0. Supplementary Table 31. ATAC-seq analysis of Mda5-KO HSCs (LSK/SLAM). Gained peaks (100 kb upstream and 25 kb downstream from the TSS) D10 versus D0. Supplementary Table 32. RNA-seq analysis of WT myeloid cells (Mac1/Gr-1). Differentially expressed genes H16 versus D0. Supplementary Table 33. RNA-seq analysis of WT myeloid cells (Mac1/Gr-1). Differentially expressed TEs H16 versus D0. Supplementary Table 34. RNA-seq analysis of Mda5-KO myeloid cells (Mac1/Gr-1). Differentially expressed genes H16 versus D0. Supplementary Table 35. RNA-seq analysis of Setdb1-knockdown versus WT HSCs (LSK/SLAM). Differentially expressed genes. Supplementary Table 36. Oligonucleotides and others.

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Clapes, T., Polyzou, A., Prater, P. et al. Chemotherapy-induced transposable elements activate MDA5 to enhance haematopoietic regeneration. Nat Cell Biol 23, 704–717 (2021). https://doi.org/10.1038/s41556-021-00707-9

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