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Altered chromatin states drive cryptic transcription in aging mammalian stem cells

Nature Aging volume 1, pages 684–697 (2021)Cite this article

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Abstract

A repressive chromatin state featuring trimethylated lysine 36 on histone H3 (H3K36me3) and DNA methylation suppresses cryptic transcription in embryonic stem cells. Cryptic transcription is elevated with age in yeast and nematodes and reducing it extends yeast lifespan, though whether this occurs in mammals is unknown. We show that cryptic transcription is elevated in aged mammalian stem cells, including murine hematopoietic and neural stem cells and human mesenchymal stem cells. Precise mapping allowed quantification of age-associated cryptic transcription in human mesenchymal stem cells aged in vitro. Regions with significant age-associated cryptic transcription have a unique chromatin signature: decreased H3K36me3 and increased H3K4me1, H3K4me3 and H3K27ac with age. Genomic regions undergoing such changes resemble known promoter sequences and are bound by TATA-binding protein, even in young cells. Hence, the more permissive chromatin state at intragenic cryptic promoters likely underlies increased cryptic transcription in aged mammalian stem cells.

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Fig. 1: Age-associated increase in cryptic transcription detected in mHSC and hMSC RNA-seq data.
Fig. 2: Analysis of RNA-seq in NSCs and other mammalian tissues suggests a widespread increase in cryptic transcription during mammalian aging.
Fig. 3: Sequencing the 5ʹ ends of capped RNA shows increased cryptic transcription during hMSC aging.
Fig. 4: Chromatin states associated with transcription are largely preserved during aging.
Fig. 5: Chromatin near cTSSs takes on promoter-like characteristics with age.
Fig. 6: Model of the mechanisms driving elevated cryptic transcription in mammalian aging.

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Data availability

All RNA-seq, ChIP-seq, WGBS and CMS-IP-seq data have been deposited in the GEO database at NCBI under accession code GSE156409.

Code availability

All code for implementing the analyses described in this paper is available at GitHub at https://github.com/NyxSLY/ASCT.

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Acknowledgements

We thank R. Chen and the Human Genome Sequence Center at Baylor College of Medicine for performing the Illumina sequencing reported here. This work was funded by National Institutes of Health grants R01AG052507 to W.D. and R01AG053268 to A.E.W.; R01HL134780 and R01HL146852 to Y.H.; CPRIT award R1306 to W.D.; and a Ted Nash Long Life Foundation research grant to W.D. B.S.M. was supported by National Institutes of Health training grant T32AG000183.

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Author notes

  1. These authors contributed equally: Brenna S. McCauley, Luyang Sun.

Authors and Affiliations

  1. Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA

    Brenna S. McCauley, Luyang Sun, Ruofan Yu, Haiying Liu & Weiwei Dang

  2. Center for Epigenetics and Disease Prevention, Institute of Bioscience and Technology, Texas A&M University, Houston, TX, USA

    Minjung Lee & Yun Huang

  3. Department of Translational Molecular Pathology, the University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Minjung Lee

  4. Department of Genetics, Stanford University, Stanford, CA, USA

    Dena S. Leeman

  5. Department of Discovery Immunology, Genentech, Inc. 1 DNA Way, South San Francisco, CA, USA

    Dena S. Leeman

  6. Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA

    Ashley E. Webb

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Contributions

Author contributions were as follows: conceptualization, W.D., B.S.M. and L.S.; methodology, W.D., B.S.M., L.S. and Y.H.; investigation, B.S.M., L.S., R.Y., M.L., H.L., D.S.L., Y.H., A.E.W. and W.D.; writing of original draft, B.S.M., L.Y. and W.D.; writing, review and editing, B.S.M., L.S., R.Y., D.S.L., Y.H., A.E.W., M.K. and W.D.; funding acquisition, W.D., Y.H. and A.E.W.; and supervision, W.D.

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Correspondence to Weiwei Dang.

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Extended data

Extended Data Fig. 1 Additional analysis of aging RNA-seq from mHSCs and hMSCs.

a, Growth curve of culture-expanded hMSCs; PD: population doubling. b, Proportion of senescence-associated β-galatosidase stained hMSCs at the indicated PDs, showing standard error of the mean. In total, 1,629, 1,641, and 293 cells were analyzed in PD 12, PD 32 and PD 42, respectively. c, Adipogenic and osteogenic differentiation of hMSCs is shown by Oil Red O (ORO) and Alizarin Red S (ARS) staining. Experiments were performed 3 times. d, Boxplots of the log2-transformed ratio of reads mapping to the indicated exon vs. reads mapping to the first or second exon (dark and light orange, respectively) in Setd2-perturbed vs. control samples (ratio in Setd2-perturbed divided by ratio in control, or ratio of ratios). Samples used: Setd2 knockout (n = 6,869, GSE72855; n = 7,821, E-GEOD-54932)11,23 or knockdown (n = 6,606, E-GEOD-51006)22 in murine embryonic stem cells and knockout in murine oocytes (n = 7,143, GSE112832)24. e,f, Boxplots showing the log2-transformed ratio of RNA-seq reads mapping to the indicated exons vs. the first exon of genes in mHSCs in e and hMSCs in f. Young samples are in blue and old in red; Y: young, O: old. g,h, Boxplots of the log2-transformed ratio of reads mapping to the indicated exons vs. reads mapping to the first exon in old vs. young samples (ratio in old divided by ratio in young, or ratio of ratios) divided by expression quartile in mHSCs in g and hMSCs in h. i, j, Bar charts of transcripts in which the indicated exon has a 2-fold increase (red) or decrease (blue) in TPM vs. the first exon; mHSCs in i and hMSCs in j. k,l, Histograms of the CT scores of major transcripts; mHSCs in k and hMSCs in l. m,n, Scatterplots showing the log2-transformed CT scores in old vs. young samples; mHSCs in m and hMSCs in n. Blue indicates an age-associated increase in cryptic transcription. o,p, Length distribution of transcripts with increased cryptic transcription (n = 210 for mHSCs and n = 305 for hMSCs) with age vs. expressed major transcripts with at least 3 exons. mHSCs are in o and hMSCs in p. For boxplots, bounds of box show the 25th and 75th percentiles; the central lines in the box plots represent the median value; and the whiskers show 1.5-fold of the interquartile range. P values were calculated using a two-sided Wilcoxon signed-rank test vs. the null hypothesis that the samples have the same average value or the log2-transformed ratio of ratios equals 1. Exact P values for panels D, G, and H are provided in Supplementary Table 1. Expressed major transcripts with at least 3 exons were included in the cryptic transcription analyses for mHSCs (n = 10,068, panels e, g, and o) and hMSCs (n = 9,230, panels f, h, and p).

Extended Data Fig. 2 Analysis of aging RNA-seq in NSCs and other tissues.

a,b, Boxplots showing the log2-transformed ratio of ratios (indicated exon vs. first exon) for transcripts in aNSCs, separated into quartiles by expression levels. aNSCs isolated from female mice are shown in a and from males in b; Y indicates young and O old. Expressed major transcripts with at least 3 exons were included in the analyses for females (n = 6,110) and males (n = 46,54). P values were calculated using a two-sided Wilcoxon signed-rank test with the null hypothesis that the calculated log2 ratios are equal to 0; exact P values are provided in Supplementary Table 1. c, Comparison of the distribution of the length of transcripts with an increase in cryptic transcription with age vs. expressed major transcripts with at least 3 exons in aNSCs, shown as a histogram and boxplot. aNSCs isolated from females on top (n = 266 for genes with age-increased cryptic transcription and n = 6,110 for all major transcripts) and from males on the bottom (n = 237 for genes with age-increased cryptic transcription and n = 4,654 for all major transcripts). P values were calculated using a two-sided Wilcoxon signed-rank test. d, Heatmap depicting the log2-transformed ratio of ratios (indicated exon vs. the first exon) from a variety of mammalian aging or senescence RNA-seq datasets, identified in the figure (E-GEOD-59966; E-GEOD-46486; GSE53330; E-MTAB-4879; and refs. 26,27,28,29,30,31,32,33,34,35). e,f, Boxplots showing the log2-transformed ratio of ratios (indicated exon vs. first or second exon) for transcripts in fibroblasts from Rett syndrome patients vs. controls36 in e and cells engineered to carry a mutation in LMNA that causes Werner syndrome37 in f. Expressed major transcripts with at least 3 exons were included in the analysis (Rett syndrome, n = 7,302; Werner syndrome MSCs, n = 8,934, Werner syndrome ESCs, n = 10,185). No significant result founds were in e and f using a two-sided Wilcoxon signed-rank test. For boxplots, bounds of box show the 25th and 75th percentiles; the central lines in the box plots represent the median value; and whiskers show 1.5-fold of the interquartile range.

Extended Data Fig. 3 Additional analysis of cryptic transcription in aging hMSCs.

a, DECAP-seq read pile ups around cTSSs that were identified as having higher DECAP-seq peaks in the young hMSC sample vs. the old, that is, sites where cryptic transcription decreases with age. b, Genes were ranked by the ratio of their FPKM in young cells vs. FPKM in old. Histograms showing the ranked distribution of genes in the following categories: all genes (top); genes with sites that have an age-associated increase in cryptic transcription (middle); and genes with sites that have a decrease in cryptic transcription with age (bottom). FC indicates fold change. c, RT-qPCR results showing a mild decrease (~30%) in SETD2 RNA levels upon SETD2 knockdown in hMSCs. d, Growth curve showing growth of hMSCs expressing a control, non-targeting (NT) shRNA vs. those expressing SETD2 shRNA. e, Complete HOMER de novo motif results of the significant motifs found from age-increased cTSS flanking regions (±200 bp). Known promoter elements are highlighted in green. The at-AC splice acceptor sequence is shown in blue. TFs that bind motifs similar to the ones identified by HOMER are shown in grey if they are not expressed in hMSCs (FPKM < 1); ones listed in black are expressed in hMSCs. TFs highlighted in bold and indicated with an asterisk show the highest age-associated changes in expression and were included in a GO analysis. The P value was directly calculated by HOMER Motif Analysis38. f, GO analysis of putative targets of the indicated transcription factors in ENCODE datasets. Gene ratio indicates the proportion of genes in the dataset that fall in the GO cluster. In all panels, cTSS: cryptic transcription start site. Enrichment P values were generated by a one-sided hypergeometric test to determine if the list contains more genes for the GO cluster than expected by chance.

Extended Data Fig. 4 Genome-wide analysis of chromatin states.

a, Emission parameters of the ten state ChromHMM model in hMSCs. b, Enrichment of the ChromHMM states in the indicated genomic regions in young hMSCs. c, As b, except in old hMSCs. d, Enrichment of the ChromHMM states around annotated TSSs and TESs in young and old hMSCs. e, Transition map of ChromHMM states in old vs. young hMSCs. State in old is along the x-axis and state in young along the y-axis. f, Two examples of a decline in H3K9me3 (ChromHMM state 1) enrichment at LADs with age. Normalized mapped reads are shown in blue for young hMSCs and in red for old. g, Distribution of the chromatin states of age-decreased cTSSs determined by DECAP-seq in young and old hMSCs. h, Methylated CpG distributions in young (left) and old (right) hMSCs. i, Number of CMS-IP-seq (5-hydroxymethylcytosine) peaks in young and old hMSCs. j, Graphical representation of a one-sided permutation test with the null hypothesis that the number of CMS-IP-seq peaks that overlap with age-increased DECAP-seq peaks is equal to the background level of CMS-IP-peaks. This shows a significant overlap of CMS-IP-seq peaks with age-increased cTSSes. In all panels, Y: young; O: old; TSS: transcription start site; TES: transcription end site; LAD: lamin-associated domain.

Extended Data Fig. 5 Chromatin state changes around age-increased cTSSes.

a, Read pile ups of H3K4me3 around annotated TSSs (left), age-increased cTSSs (middle), and age-decreased cTSSs (right), independently clustered into 3 groups. b, As in a, except H3K4me1 enrichment is shown. c, As in a, except depicting H3K27ac enrichment. d, Boxplots showing H3K36me3 enrichment around age-increased cTSSs (n = 1,373) and endogenous TSSs (n = 13,802) in young and old hMSCs. P values were calculated using a two-sided Wilcoxon signed-rank test with the null hypothesis that enrichment was equal in the young and old samples. e, Bar chart showing the proportion of TBP ChIP-seq peaks around endogenous TSSs in young and old hMSCs. f, Metagene plot of TBP enrichment around annotated TSSs in hMSCs. g, DECAP-seq signal around putative age-associated cTSSs predicted in hMSCs by the chromatin state model. Averaged read depth of putative age-associated promoter regions (±1 kb of the midpoint of the identified region) in young (blue) and old (red) is shown on the left at 100 bp resolution; a boxplot of the log2-transformed ratio of signal in old vs. signal in young shown on the right (n = 166). Distinct random genic non-promoter regions (length = 2 kb) were used as control (n = 2,774). P values were calculated using a two-sided Wilcoxon signed-rank test vs. the hypothesis that the RNA-seq ratios were equal in the putative age-increased cTSSs vs. control regions, as appropriate. Regions without DECAP-seq signal were excluded from analysis. In all panels, Y: young; O: old; TSS: transcription start site; cTSS: cryptic transcription start site. For boxplots, bounds of box show the 25th and 75th percentiles; the central lines in the box plots represent the median value; and whiskers show 1.5-fold of the interquartile range.

Supplementary information

Supplementary Information

Supplementary Fig. 1 and associated legend.

Reporting Summary

Supplementary Tables

Contains Supplementary Table 1 (exact P values for Figs. 1 and 2 and Extended Data Figs. 1 and 2); Supplementary Table 2 (DECAP-seq peak and gene lists for DAVID analysis); Supplementary Table 3 (DAVID analysis of DECAP-seq peaks in young hMSCs); Supplementary Table 4 (DAVID analysis of DECAP-seq peaks in old hMSCs); Supplementary Table 5 (DAVID analysis of age-increased DECAP-seq peaks); and Supplementary Table 6 (GO analysis of target genes of cryptic transcription-associated TFs in ENCODE datasets).

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McCauley, B.S., Sun, L., Yu, R. et al. Altered chromatin states drive cryptic transcription in aging mammalian stem cells. Nat Aging 1, 684–697 (2021). https://doi.org/10.1038/s43587-021-00091-x

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