Extensive and coordinated transcription of noncoding RNAs within cell-cycle promoters

Journal name:
Nature Genetics
Year published:
Published online


Transcription of long noncoding RNAs (lncRNAs) within gene regulatory elements can modulate gene activity in response to external stimuli, but the scope and functions of such activity are not known. Here we use an ultrahigh-density array that tiles the promoters of 56 cell-cycle genes to interrogate 108 samples representing diverse perturbations. We identify 216 transcribed regions that encode putative lncRNAs, many with RT-PCR–validated periodic expression during the cell cycle, show altered expression in human cancers and are regulated in expression by specific oncogenic stimuli, stem cell differentiation or DNA damage. DNA damage induces five lncRNAs from the CDKN1A promoter, and one such lncRNA, named PANDA, is induced in a p53-dependent manner. PANDA interacts with the transcription factor NF-YA to limit expression of pro-apoptotic genes; PANDA depletion markedly sensitized human fibroblasts to apoptosis by doxorubicin. These findings suggest potentially widespread roles for promoter lncRNAs in cell-growth control.

At a glance


  1. Identification of ncRNAs near and within cell-cycle genes.
    Figure 1: Identification of ncRNAs near and within cell-cycle genes.

    (a) Flow chart of the strategy for systematic discovery of cell-cycle ncRNAs. (b) Representative tiling array data. RNA hybridization intensity and H3K36me3 and H3K4me3 ChIP-chip signals relative to the input at the CCNE1 locus in human fetal lung fibroblasts. The predicted transcripts are shown in red boxes. Known mRNA exons are shown in black boxes. Each bar represents a significant peak from one of the 108 array channels. (c) Chromatin state at the transcribed regions. The average ChIP-chip signal relative to the input calculated across transcriptional peaks expressed in human fetal lung fibroblasts with or without doxorubicin treatment. (d) Codon substitution frequency (CSF) analysis. Graph of the average evolutionary CSF of the exons of coding genes and their predicted transcripts. CSF < 10 indicates no protein coding potential. (e) Transcriptional landscape of cell-cycle promoters. We aligned all cell-cycle promoters at the TSS and calculated the average RNA hybridization signal across the 12-kb window. The output represents a 150-bp running window of average transcription signals across all 54 arrays. See also Supplementary Table 1 and Supplementary Figure 1.

  2. ncRNA expression across diverse cell cycle perturbations.
    Figure 2: ncRNA expression across diverse cell cycle perturbations.

    (a) Hierarchical clustering of 216 predicted ncRNAs across 54 arrays, representing 108 conditions. Red indicates that the cell cycle perturbation induced transcription of the ncRNA. Green indicates that the cell cycle perturbation repressed transcription of the ncRNA. Black indicates no significant expression change. (b) Close up view of the ncRNAs in cluster 1. See also Supplementary Tables 2,3.

  3. Functional associations of ncRNAs.
    Figure 3: Functional associations of ncRNAs.

    (a) lncRNA expression patterns do not correlate with those of the mRNAs in cis. Histogram of Pearson correlations between each of the 216 ncRNAs and the cis mRNA across 108 samples. (b) lncRNA expression patterns have a positive correlation with neighboring lncRNA transcripts. Histogram of Pearson correlations between each of the 216 ncRNAs and nearby transcripts on the same locus across 108 samples. (c) Genes co-expressed with lncRNAs are enriched for functional groups in the cell cycle and in DNA damage response. Module map of lncRNA gene sets (columns) versus Gene Ontology Biological Processes gene sets (rows) across 17 samples (P < 0.05, false discovery rate <0.05). A yellow entry indicates that the Gene Ontology gene set is positively associated with the lncRNA gene set. A blue entry indicates that the Gene Ontology gene set is negatively associated with the lncRNA gene set. A black entry indicates no significant association. Representative enriched Gene Ontology gene sets are listed.

  4. Validated expression of ncRNAs in cell cycle progression, ESC differentiation and human cancers.
    Figure 4: Validated expression of ncRNAs in cell cycle progression, ESC differentiation and human cancers.

    We generated custom TaqMan probes and used them to interrogate independent biological samples for lncRNA expression. (a,b) Periodic expression of lncRNAs (blue) during synchronized cell cycle progression in HeLa cells (a) and foreskin fibroblasts (b). Cell cycle phases were confirmed by fluorescence-activated cell sorting and expression of genes with known periodic expression in the cell cycle (orange). (c) Regulated expression of lncRNAs in human ESCs compared to fetal pancreas. D, day. (d) Differential expression of lncRNAs in normal breast epithelium compared to breast cancer samples.

  5. ncRNAs at the CDKN1A locus are induced by DNA damage.
    Figure 5: ncRNAs at the CDKN1A locus are induced by DNA damage.

    (a) At the top is a map of all detected transcripts at the CDKN1A promoter. In the middle two tracks are examples of RNA hybridization intensity in the control or in 24 h doxorubicin (dox) treated (200 ng/ml) human fetal lung fibroblasts. Note that we did not observe all DNA-damage–inducible transcripts in one single time point. At the bottom, the p53 ChIP-chip signal relative to input confirmed the p53 binding site immediately upstream of the CDKN1A TSS after DNA damage. The RACE clone of upst:CDKN1A:−4,845 closely matches the predicted transcript on the tiling array. See also Supplementary Figure 7. (b) Quantitative RT-PCR of lncRNAs shows coordinate induction or repression across a 24 h time course of doxorubicin treatment. A cluster of lncRNAs transcribed from the CDKN1A locus are induced. (c) Expression of transcripts from the CDKN1A locus over a 24 h time course after doxorubicin treatment of normal human fibroblasts (FL3). See also Supplementary Figure 6. (d) RNA blot of PANDA confirms a transcript size of 1.5 kb. (e) Doxorubicin induction of PANDA requires p53 but not CDKN1A. Mean ± s.d. are shown; *P < 0.05 relative to siCTRL (control siRNA) determined by student's t-test. (f) Expression of wild-type p53 in p53-null H1299 cells restores DNA damage induction of CDKN1A and PANDA. The p53 (p.Val272Cys) loss-of-function mutant fails to restore induction, whereas a gain-of-function Li-Fraumeni allele, p53 (p.Arg273His), selectively retains the ability to induce PANDA.

  6. PANDA lncRNA regulates the apoptotic response to DNA damage.
    Figure 6: PANDA lncRNA regulates the apoptotic response to DNA damage.

    (a) siRNA knockdown of PANDA in the presence of DNA damage with doxorubicin in human fibroblasts (FL3). Custom siRNAs specifically target PANDA with no discernable effect on the LAP3 mRNA. Mean ± s.d. are shown in all bar graphs. *P < 0.05 compared to siCTRL for all panels determined by Student's t-test. (b) Heat map of gene expression changes with siPANDA relative to control siRNA after 24 h of doxorubicin treatment in FL3 cells. (c) Quantitative RT-PCR of canonical apoptosis pathway genes revealed induction with siPANDA relative to control siRNA after 28 h of doxorubicin treatment (in FL3 cells). (d) Quantitative RT-PCR of CDKN1A and TP53 in FL3 cells revealed no reduction in expression with siPANDA relative to control siRNA. (e) TUNEL immunofluorescence of control and siPANDA FL3 fibroblasts after 28 h of doxorubicin treatment. Scale bar, 20 μm. (f) Quantification of three independent TUNEL assays. P < 0.05 for each siPANDA sample compared to siCTRL determined by student's t-test. (g) Protein blot of PARP cleavage in control and PANDA siRNA FL3 fibroblasts after 24 h of doxorubicin treatment.

  7. PANDA regulates transcription factor NF-YA.
    Figure 7: PANDA regulates transcription factor NF-YA.

    (a) RNA chromatography of PANDA from doxorubicin-treated FL3 cell lysates. We visualized the retrieved proteins by immunoblot analysis. (b) Immunoprecipitation of NF-YA from doxorubicin-treated FL3 lysates specifically retrieves PANDA, as measured by qRT-PCR. Immunoblot confirms immunoprecipitation of NF-YA, as shown at the bottom. (c) ChIP of NF-YA in FL3 fibroblasts nucleofected with siCTRL or siPANDA. ChIP-qPCR at known NF-YA target sites on promoters of CCNB1, FAS, NOXA, BBC3 (PUMA) or a control downstream region in the FAS promoter lacking the NF-YA motif. Mean ± s.d. are shown in all bar graphs. *P < 0.05 determined by Student's t-test. (d) Concomitant knockdown of NF-YA attenuates induction of apoptotic genes by PANDA depletion, as measured by qRT-PCR. For knockdown efficiency see Supplementary Figure 11. (e) Concomitant knockdown of NF-YA rescues apoptosis induced by PANDA depletion. Quantification of TUNEL staining is shown. The legend for this panel is as in d.

  8. Model of coding and noncoding transcripts at the CDKN1A locus coordinating the DNA damage response.
    Figure 8: Model of coding and noncoding transcripts at the CDKN1A locus coordinating the DNA damage response.

    After DNA damage, p53 binding at the CDKN1A locus coordinately activates transcription of CDKN1A as well as noncoding transcripts PANDA and linc-p21. CDKN1A mediates cell cycle arrest, PANDA blocks apoptosis through NF-YA, and linc-p21 mediates gene silencing through recruitment of hnRPK.

Accession codes

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Author information

  1. These authors contributed equally to this work.

    • David J Wong &
    • Howard Y Chang


  1. Program in Epithelial Biology, Stanford University School of Medicine, Stanford, California, USA.

    • Tiffany Hung,
    • Ashley K Koegel,
    • David J Wong &
    • Howard Y Chang
  2. Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California, USA.

    • Tiffany Hung,
    • Ashley K Koegel,
    • Seung K Kim &
    • Howard Y Chang
  3. Life Technologies, Foster City, California, USA.

    • Yulei Wang,
    • Yu Wang &
    • Benjamin Kong
  4. The Broad Institute, Cambridge, Massachusetts, USA.

    • Michael F Lin &
    • Manolis Kellis
  5. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Michael F Lin &
    • Manolis Kellis
  6. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

    • Yojiro Kotake &
    • Yue Xiong
  7. Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

    • Yojiro Kotake
  8. Department of Biochemistry 1, Hamamatsu University School of Medicine, Higashi-ku, Hamamatsu, Japan.

    • Yojiro Kotake
  9. Department of Genetics, Dartmouth Medical School, Hanover, New Hampshire, USA.

    • Gavin D Grant &
    • Michael L Whitfield
  10. Department of Pathology, Academic Medical Center, Amsterdam, The Netherlands.

    • Hugo M Horlings &
    • Marc van de Vijver
  11. Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Nilay Shah &
    • Saraswati Sukumar
  12. Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Christopher Umbricht
  13. Department of Developmental Biology, Stanford University School of Medicine, Stanford, California, USA.

    • Pei Wang &
    • Seung K Kim
  14. Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Montebello, Oslo, Norway.

    • Anita Langerød &
    • Anne-Lise Børresen-Dale
  15. Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.

    • Anne-Lise Børresen-Dale


H.Y.C. and D.J.W. initiated the project. H.Y.C., D.J.W. and T.H. designed the experiments. T.H. performed the experiments and the computational analysis. Yulei Wang, Yu Wang and B.K. conducted high-throughput TaqMan RT-PCRs. M.F.L. and M.K. contributed CSF analysis. The following authors contributed samples or reagents: A.K.K., Y.K., G.D.G., H.M.H., N.S., C.U., P.W., A.L., S.K.K., M.v.d.V., A.-L.B.-D., S.S., M.L.W. and Y.X. The manuscript was prepared by H.Y.C., T.H. and D.J.W. with input from all co-authors.

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The authors declare no competing financial interests.

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Supplementary information

PDF files

  1. Supplementary Text and Figures (1M)

    Supplementary Figures 1–11 and Supplementary Tables 1, 2 and 5.

Excel files

  1. Supplementary Table 3 (29K)

    List of cell cycle promoter transcripts

  2. Supplementary Table 4 (111K)

    Combined expression of all transcripts across all tiling arrays

Additional data