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Reactivation of senescence-associated endogenous retroviruses by ATF3 drives interferon signaling in aging

Nature Aging volume 4, pages 1794–1812 (2024)Cite this article

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Abstract

Reactivation of endogenous retroviruses (ERVs) has been proposed to be involved in aging. However, the mechanism of reactivation and contribution to aging and age-associated diseases is largely unexplored. In this study, we identified a subclass of ERVs reactivated in senescent cells (termed senescence-associated ERVs (SA-ERVs)). These SA-ERVs can be bidirectional transcriptionally activated by activating transcription factor 3 (ATF3) to generate double-stranded RNAs (dsRNAs), which activate the RIG-I/MDA5–MAVS signaling pathway and trigger a type I interferon (IFN-I) response in senescent fibroblasts. Consistently, we found a concerted increased expression of ATF3 and SA-ERVs and enhanced IFN-I response in several tissues of healthy aged individuals and patients with Hutchinson–Gilford progeria syndrome. Moreover, we observed an accumulation of dsRNAs derived from SA-ERVs and higher levels of IFNβ in blood of aged individuals. Together, these results reveal a previously unknown mechanism for reactivation of SA-ERVs by ATF3 and illustrate SA-ERVs as an important component and hallmark of aging.

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Fig. 1: Activation of RIG-I/MDA5–MAVS and IFN-I signaling in senescent cells.
Fig. 2: SA-ERV activation and dsRNA accumulation in senescent cells.
Fig. 3: SA-ERVs generate dsRNAs through bidirectional transcription in senescent cells.
Fig. 4: ATF3 is upregulated in senescent cells and activates SA-ERVs associated with RIG-I/MDA5–MAVS and IFN-I signaling.
Fig. 5: ATF3 promotes senescence by activating IFN-I signaling.
Fig. 6: The expression of SA-ERVs is significantly positively correlated with age.
Fig. 7: SA-ERVs and IFN-I signaling are activated in WS and HGPS.
Fig. 8: Accumulation of dsRNAs derived from SA-ERVs and activation of RIG-I/MDA5–MAVS and IFN-I signaling in aging.

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Data availability

All datasets have been deposited in the Genome Sequence Archive for Human (GSA-Human) in the National Genomics Data Center70 with accession numbers as indicated: raw RNA-seq data for non-senescent and senescent WI38 cells, HRA005428; raw dsRIP-seq data for senescent WI38 cells, HRA007306; raw WGBS data for non-senescent and senescent WI38 cells, HRA005437; raw ATF3 ChIP-seq data for senescent WI38 cells, HRA007303; raw RNA-seq data for WI38 cells with ATF3 ectopic expression, HRA005436; raw RNA-seq data for whole blood from young and old individuals, HRA005441. Additional public datasets used in this study are listed in Supplementary Table 5. Source data are provided with this paper. All other data supporting the conclusions of the study are available upon reasonable request from the corresponding authors.

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Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (U22A20318 and 31730020 to Y.-S.C., 32000512 to J.M. and 31801155 to Q.Z.), the Zhejiang Provincial Department of Science and Technology (2023C03166 to Y.-S.C.) and the Interdisciplinary Research Project of Hangzhou Normal University (2024JCXK06 to Y.-S.C.).

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Author notes

  1. These authors contributed equally: Jian Mao, Qian Zhang, Yang Zhuang.

Authors and Affiliations

  1. Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Zhejiang Key Laboratory of Medical Epigenetics, Hangzhou Normal University School of Basic Medical Sciences, Hangzhou, China

    Jian Mao, Qian Zhang, Yang Zhuang, Yinyu Zhang, Juan Pan, Yuxuan Ding, Miao Wang & Yu-Sheng Cong

  2. Department of Clinical Laboratory, Zhuji People’s Hospital of Zhejiang Province, Shaoxing, China

    Linmeng Li

  3. Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Hangzhou Normal University School of Nursing, Hangzhou, China

    Lu Xu

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Contributions

J.M., Q.Z. and Y.-S.C. conceived of and designed the experiments. J.M., Q.Z. and Y. Zhuang performed most of the experiments and data analyses. Y. Zhang, L.L., J.P., L.X., Y.D. and M.W. helped with experiments. J.M., Q.Z. and Y.-S.C. wrote the paper.

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Correspondence to Jian Mao or Yu-Sheng Cong.

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Nature Aging thanks Vera Gorbunova and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data

Extended Data Fig. 1 Activation of RIG-I/MDA5-MAVS and IFN-I signaling in old individuals and senescent cells.

a, Differential expression analysis of genes related to viral defense and IFN-I response in adipose tissue, brain, muscle, nerve, skin (not sun exposed), and whole blood between old versus young individuals from GTEx database. b, GSEA enrichment plot (left), enriched biological processes (BP) (middle), and heatmap of genes related to RIG-I/MDA5-MAVS and IFN-I signaling (right) in non-senescent and senescent IMR90 cells through RNA-seq analysis. c, GSEA enrichment plot (left), enriched biological processes (BP) (middle), and heatmap of genes related to RIG-I/MDA5-MAVS and IFN-I signaling (right) in non-senescent and senescent BJ cells through RNA-seq analysis. d, RT-qPCR analysis of genes related to RIG-I/MDA5-MAVS and IFN-I signaling in non-senescent and senescent WI38 or IMR90 cells. Data represent mean ± SEM of three biological replicates. P values were calculated by moderated t-test and adjusted by the Benjamini-Hochberg method in limma (a), accumulative hypergeometric test (b, middle), Wald test with Benjamini-Hochberg adjustment in DESeq2 (b, right), or unpaired two-tailed Student’s t-test (d). NES, normalized enrichment score. non-Sen, non-senescent. Sen, senescent.

Source data

Extended Data Fig. 2 RNA-seq and ATAC-seq analysis for ERVs in non-senescent and senescent WI38, IMR90, and BJ cells.

a, Scatterplot representing differences in ERVs expression in non-senescent and senescent BJ cells by RNA-seq analysis. Red dots indicate ERV elements with significantly increased expression, while blue dots indicate ERV elements with significantly decreased expression. b, Regression analysis of the correlation for the log2FoldChange (senescent versus non-senescent) of ERV elements expression between WI38 and BJ cells. R is Pearson correlation coefficient. P value was calculated using a two-sided Pearson’s correlation test. c, The profiles for genomic regions of ERV1, ERVK, ERVL, and ERVL-MaLR subfamilies by ATAC-seq analysis in BJ cells. d, Heatmap showing expression by RNA-seq analysis (left) and chromatin accessibility by ATAC-seq analysis (right) of ERV elements in non-senescent and senescent BJ cells. e, Venn diagram showing the overlap of significantly activated ERV elements in senescent WI38, IMR90, and BJ cells. f, log2FoldChange differences of chromatin accessibility (left) and expression (right) of LTR39, MER61C, MER61-int, MSTC, and MST-int, between senescent and non-senescent BJ cells, revealed by ATAC-seq and RNA-seq respectively. g, Signal distributions of ATAC-seq and RNA-seq at the representative genomic loci of LTR39, MER61C, MER61-int, MSTC, and MST-int, in non-senescent and senescent BJ cells. P values were calculated by Wald test in DESeq2 (a and f). non-Sen, non-senescent. Sen, senescent.

Source data

Extended Data Fig. 3 Expression of SA-ERVs is gradually upregulated with passaging in normal fibroblasts, but remains unchanged in hTERT-immortalized cells.

a, The expression patterns of LTR39, MER61C, MER61-int, MSTC, and MST-int during passaging in WI38 and HFF cells by RNA-seq analysis. b, The expression of SA-ERVs (left), and genes related to RIG-I/MDA5-MAVS and IFN-I signaling (right), in PD46 and PD109 hTERT-immortalized WI38 cells by RNA-seq analysis. c, The profiles of ATAC-seq signals in genomic regions of ERV1, ERVK, ERVL, and ERVL-MaLR subfamilies in PD46 and PD109 hTERT-immortalized WI38 cells. Data represent mean ± SEM of three biological replicates (a and b). PD, population doublings.

Source data

Extended Data Fig. 4 Treatments with DNMTi DAC activate RIG-I/MDA5-MAVS and IFN-I signaling in human non-senescent fibroblasts.

a, log2FoldChange differences of CpG methylation levels of genomic LTR39, MER61C, MER61-int, MSTC, and MST-int loci between senescent versus non-senescent WI38 or IMR90 cells by WGBS. b, Heatmap of CpG methylation levels in the genomic regions of ERV1, ERVK, ERVL, and ERVL-MaLR subfamilies in human primary dermal fibroblasts (HDFs) with DAC treatment and control by RRBS analysis. c, Scatterplot representing differences in ERVs expression between HDFs with DAC treatment versus control by RNA-seq analysis. Red dots indicate ERV elements with significantly increased expression, while blue dots indicate ERV elements with significantly decreased expression. d, GSEA enrichment plots between HDFs with DAC treatment versus control. e, Heatmap showing expression of genes related to RIG-I/MDA5-MAVS and IFN-I signaling in HDFs with DAC treatment and control. f, Scatterplot representing differences in ERVs expression between human gingival fibroblasts (HGFs) with DAC treatment versus control by RNA-seq analysis. Red dots indicate ERV elements with significantly increased expression, while blue dots indicate ERV elements with significantly decreased expression. g, Expression analysis of sense (upper) and antisense (lower) transcripts of the SA-ERVs in HGFs between DAC treatment and control, using strand-specific RNA-seq. Data represent mean ± SEM. n = 5 biological replicates per group. h, GSEA enrichment plots between HGFs with DAC treatment versus control. i, Heatmap showing expression of genes related to RIG-I/MDA5-MAVS and IFN-I signaling in HGFs with DAC treatment and control. P values were calculated by Wald test in DESeq2 (c, e, f, and i), or paired two-tailed Student’s t-test (g).

Source data

Extended Data Fig. 5 Analysis for the functional characteristics of ATF3.

a, Motif analysis based on the sequences of SA-ERVs. P value was calculated by binomial test in HOMER. b, Scatterplot representing the enrichment of ATF3 on genomic ERV elements. Red dots indicate ERV elements with ATF3 binding. There are 357 ERV elements potentially bound by ATF3. c, Analysis for enrichment of ATF3 on the promoters of IFIH1, RIGI, IFNB1, OAS1, and CCL2 in senescent WI38 cells by ChIP-seq. β-actin promoter as negative control. d, ChIP-qPCR analysis for ATF3 targets on the promoters of IFIH1, RIGI, IFNB1, OAS1, and CCL2 in senescent WI38 cells. LTR39 and β-actin promoter were used as positive and negative controls, respectively. Data represent mean ± SEM of three biological replicates. P values were calculated by two-way ANOVA with Sidak’s multiple comparisons test. e, Scatterplot representing differences of genes expression in WI38 cells expressing ATF3 versus control by RNA-seq analysis. Red dots indicate significantly upregulated genes, while blue dots indicate significantly downregulated genes. P values were calculated by Wald test with Benjamini-Hochberg adjustment in DESeq2. f, Enriched biological processes (BP) for WI38 cells expressing ATF3 versus control. GO was analyzed for downregulated genes in WI38 cells expressing ATF3 compared to control. P values were calculated by the accumulative hypergeometric test.

Source data

Extended Data Fig. 6 ERVs and IFN-I signaling are activated in aged mice.

a, RT-qPCR analysis of mouse ERVs in kidney or lung from young and aged mice. b, The profiles for genomic regions of ERV1, ERVK, ERVL, and ERVL-MaLR subfamilies by ATAC-seq analysis for mouse kidney. c, Enrichment of dsRNA over ssRNA calculated by normalizing the ΔCt between RNase A treated and non-treated of mouse ERVs (dsRNA) against β-actin (ssRNA). SINEB1 and Rplp0 served as positive and negative controls, respectively. d, ELISA analysis of dsRNA in total RNA extracted from kidney or lung of young and aged mice. e, Western blot analysis of ATF3, RIG-I, MDA5, and MAVS in kidney or lung from young and aged mice. β-actin served as loading control. f, RT-qPCR analysis of genes related to RIG-I/MDA5-MAVS and IFN-I signaling in kidney or lung from young and aged mice. g, GSEA enrichment plots showing “KEGG: RIG-I-like receptor signaling pathway” enriched signature between aged versus young mice. NES, normalized enrichment score. h, log2FoldChange expression differences of genes related to RIG-I/MDA5-MAVS and IFN-I signaling in kidney or lung between young and aged mice by RNA-seq analysis. Data represent mean ± SEM. n = 5 samples per group (a, c, d, and f). P values were calculated by unpaired two-tailed Student’s t-test (a, c, d, and f), or Wald test in DESeq2 (h).

Source data

Extended Data Fig. 7 Schematic representation of key findings.

Schematic model showing that SA-ERVs activated by ATF3 form dsRNAs through bidirectional transcription, drive IFN-I response via RIG-I/MDA5-MAVS signaling pathway in senescent cells, thereby contributing to SASP and other senescence-associated phenotypes. non-Sen, non-senescent. Sen, senescent.

Supplementary information

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 1

Unprocessed western blots.

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Unprocessed western blots.

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Mao, J., Zhang, Q., Zhuang, Y. et al. Reactivation of senescence-associated endogenous retroviruses by ATF3 drives interferon signaling in aging. Nat Aging 4, 1794–1812 (2024). https://doi.org/10.1038/s43587-024-00745-6

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