Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Aug 31;128(9):4132-4147.
doi: 10.1172/JCI96520. Epub 2018 Aug 20.

Cyclin D1 Overexpression Induces Global Transcriptional Downregulation in Lymphoid Neoplasms

Affiliations
Free PMC article

Cyclin D1 Overexpression Induces Global Transcriptional Downregulation in Lymphoid Neoplasms

Robert Albero et al. J Clin Invest. .
Free PMC article

Abstract

Cyclin D1 is an oncogene frequently overexpressed in human cancers that has a dual function as cell cycle and transcriptional regulator, although the latter is widely unexplored. Here, we investigated the transcriptional role of cyclin D1 in lymphoid tumor cells with cyclin D1 oncogenic overexpression. Cyclin D1 showed widespread binding to the promoters of most actively transcribed genes, and the promoter occupancy positively correlated with the transcriptional output of targeted genes. Despite this association, the overexpression of cyclin D1 in lymphoid cells led to a global transcriptional downmodulation that was proportional to cyclin D1 levels. This cyclin D1-dependent global transcriptional downregulation was associated with a reduced nascent transcription and an accumulation of promoter-proximal paused RNA polymerase II (Pol II) that colocalized with cyclin D1. Concordantly, cyclin D1 overexpression promoted an increase in the Poll II pausing index. This transcriptional impairment seems to be mediated by the interaction of cyclin D1 with the transcription machinery. In addition, cyclin D1 overexpression sensitized cells to transcription inhibitors, revealing a synthetic lethality interaction that was also observed in primary mantle cell lymphoma cases. This finding of global transcriptional dysregulation expands the known functions of oncogenic cyclin D1 and suggests the therapeutic potential of targeting the transcriptional machinery in cyclin D1-overexpressing tumors.

Keywords: Cancer; Cell Biology; Transcription.

Conflict of interest statement

Conflict of interest: The authors have declared that no conflict of interest exists.

Figures

Figure 1
Figure 1. Cyclin D1 binds genome-wide in MCL cell lines.
(A) Venn diagram representing cyclin D1 ChIP-Seq peaks in 4 MCL cell lines. (B) Distribution of cyclin D1–interacting regions over specific genomic regions in MCL cell lines. Box plots showing cyclin D1 tag density of the different genomic regions and pie charts displaying the genomic distribution of genomic intervals, with a number of tags higher than the mean. The distribution across the human genome is represented as a control. (C) Venn diagram representing cyclin D1–targeted genes identified by ChIP-Seq in MCL cell lines. Genes were considered targets when they displayed cyclin D1–binding sites located within 1 kb upstream of their TSS. (D) Average signal profile of cyclin D1 around the TSS (±3 kb) in MCL cell lines. (E) Top hits of the functional annotation clustering analysis of common cyclin D1 target genes among the 4 MCL cell lines. Only the genes with the most significant peaks in their promoters (–log P > 350) were considered for the analysis. (F) Genome browser view of the ChIP-Seq tag density plots of 4 representative cyclin D1 target genes. (G) ChIP-qPCR validation of 8 selected cyclin D1 target genes in GRANTA-519. The fold change enrichments relative to a negative region are presented (mean ± SEM) (n = 2).
Figure 2
Figure 2. Cyclin D1 occupancy correlates with active promoter marks and open chromatin conformation.
(A) Heatmap showing the ChIP-Seq tag density of cyclin D1, H3K27ac, H3K4me3, H3K4me1, and DNase I cutting sites around all genomic TSS in Z-138 cells. Each row represents a gene centered on the TSS (±5 kb). Promoters are sorted by cyclin D1 number of tags. Cyclin D1–bound (top) and –unbound (bottom) genes are shown. (B) Pie chart representing common regions bound by cyclin D1, H3K27ac, and H3K4me3 marks. Only cyclin D1 peaks at promoters (–5 kb TSS) in Z-138 cells are shown. (C) Cyclin D1 occupancy in active promoters and enhancers. Percentage of active promoters (H3K4me3+) and enhancers (H3K4me1+, H3K4me3) colocalizing with cyclin D1 in active regions (defined by H3K27ac presence) are shown. (D) Box plot showing cyclin D1 number of tags in active promoters and active enhancers. The number of all cyclin D1 peaks is represented as control. ***P < 2.2 × 10–16, Student’s t test, Holm-Bonferroni correction.
Figure 3
Figure 3. Cyclin D1 binding correlates with gene expression levels.
(A) Distribution of genes showing cyclin D1 peaks within their promoters (5 kb upstream of the TSS) according to their respective gene expression levels. All genes were sorted into 50 equal bins based on their expression levels. Results are shown as mean ± SEM of all 4 MCL cell lines. (B) Linear correlation between cyclin D1 binding and transcription. Genes were sorted as in A. The average of cyclin D1 ChIP-Seq normalized tag densities at promoters and the RPKM-normalized expression levels are shown for each bin. Spearman’s correlation, ρ = 0.98, P < 2.2 × 10–16. (C) Profile of cyclin D1 occupancy around the TSS in Z-138 cells. Genes were divided into 10 groups based on their expression levels (from higher to lower expression). The distribution of the cyclin D1 ChIP-Seq tag density average around the TSS (±1 kb) is displayed for each group. (D) Linear correlation between cyclin D1 binding in MCL cell lines and gene expression in MCL primary samples (n = 122). Genes were sorted into 50 equal bins based on their expression in MCL samples. For each bin, the cyclin D1 ChIP-Seq tag density average in the MCL cell lines and the gene expression mean in primary samples are shown. Spearman’s correlation, ρ = 0.97, P < 2.2 × 10–16. (E) Heatmap showing the cyclin D1 ChIP-Seq tag density within gene promoters of JVM13-cD1T286A and MCL cell lines. Each row represents a gene centered on the TSS (±5 kb). Promoters are sorted by the number of cyclin D1 tags in Z-138 cells. (F) Linear correlation between cyclin D1 binding and gene expression in JVM13-cD1T286A cells. Genes were sorted into 50 equal bins as in B. Spearman’s correlation, ρ = 0.97, P < 2.2 × 10–16.
Figure 4
Figure 4. Cyclin D1 overexpression results in a reduction in the total RNA content in malignant lymphoid cells.
(A) Cyclin D1 protein in JVM13-control (JVM13-ctrl), JVM13-D1, and JVM13-D1T286A cells. α-Tubulin was used as loading control. (B) Total RNA content extracted from 106 cells. Results are shown relative to the control (mean ± SEM, n = 9). *P < 0.05, Student’s t test. (C) RNA quantification by pyronin Y staining in JVM13 inducible cell lines. Only cells in G1 phase were analyzed. Left panel: FACS profile of a representative experiment. Right panel: Bar graph displaying the pyronin Y mean signal. Results are shown relative to the control (mean ± SEM, n = 3). *P < 0.05, **P < 0.01, Student’s t test. (D) Cyclin D1 expression in control (shCtrl) and cyclin D1–depleted (shCycD1 #1 and #2) GRANTA-519 cells. α-Tubulin was used as loading control. (E) Total RNA content in cyclin D1–depleted GRANTA-519 cells as in A. Results are shown relative to the control (mean ± SEM, n = 8), **P < 0.01, ***P < 0.001, Student’s t test. (F) RNA quantification by pyronin Y staining in control and cyclin D1–depleted GRANTA-519 cells as in B. Left and right panels as in C. Results are shown relative to the control (mean ± SEM, n = 4), **P < 0.01, ***P < 0.001, Student’s t test. (G) Correlation between cyclin D1 protein levels and pyronin Y staining in MCL cell lines and cell models. Mean ± SEM, n = 4; P = 4.77 × 10–4, mixed-effects models. (H) Pyronin Y intensity of 7 MM cell lines. The cell lines are represented by squares shaded according cyclin D1 levels. (I and J) Quantification of nuclear EU intensity after 24 hours of cyclin D1 induction in JVM13 cell models (n = 2) (I) or following cyclin D1 silencing in the GRANTA-519 cell line (n = 2) (J). **P < 0.01, ***P < 0.001, Student’s t test. Holm-Bonferroni correction for multiple comparisons was applied to B, C, E, and F.
Figure 5
Figure 5. Cyclin D1 overexpression produces a global downmodulation of mRNAs in lymphoid cells.
(A) Box plot displaying nCounter-based gene expression data of a 48-gene panel analyzed in JVM13-Ctrl and JVM13-cD1T286A cells. Cell extracts from 3 different amounts of cells, counted by cell cytometry, are represented on the x axis. The nCounter counts of expressed transcripts (counts >30) are shown in log2 scale on the y axis (n = 2). ***P < 2 × 10–16, Student’s paired t test. (B) Box plot displaying the mean gene expression level in the JVM13-D1T286A line of genes expressed in the cancer panel according to cyclin D1 tag density at promoters (–5 kb, TSS) in JVM13-cD1T286A cells distributed in 4 quartiles (Q1–Q4). Cell extracts from 4 × 104 cells were analyzed (n = 2). P = 1.7 × 10–6, ANOVA. (C) Box plot displaying the mean gene expression level in the JVM13-Ctrl and JVM13-D1T286A inducible cell lines of genes expressed in the cancer panel. Cell extracts from 4 × 104 cells were analyzed (n = 2). ***P < 2 × 10–16, Student’s paired t test. (D) Bar plots displaying the gene expression ratio between JVM13-Ctrl and JVM13-D1T286A inducible cell lines. Genes, both upregulated (gray) and downregulated (red), are sorted from the highest to the lowest expression ratio over JVM13-Ctrl.
Figure 6
Figure 6. Cyclin D1 colocalizes with RNA Pol II and promotes an increase in the Pol II pausing index.
(A) Correlation between normalized cyclin D1 ChIP-Seq tag density in JVM13-cD1T286A and Pol II ChIP-Seq tag density at promoters in JVM13-ctrl and JVM13-D1T286A cells. Promoters were sorted into 50 equal-sized groups based on ChIP-seq tag densities of cyclin D1. The x axis represents mean cyclin D1 normalized tags of the promoters in JVM13-cD1T286A cell lines. The y axis represents Pol II tag density in both cell lines. The linear regression line between cyclin D1 and Pol II presence in promoters is shown. (B) Average signal profiling of Pol II occupancy around the TSS (±3 kb) in JVM13-ctrl and JVM13-D1T286A inducible cell lines. The cyclin D1–binding profile in JVM13-cD1T286A cells is also shown. (C) Western blot showing different phosphorylated forms of Pol II in JVM13-ctrl, JVM13-D1T286A, and JVM13-D1 inducible cell lines. Gels were run in duplicate for the study of the phosphorylation forms. A representative Western blot (n = 3) for each antibody is presented. α-Tubulin of only one of the gels run in duplicate is shown as loading control. (D) Plot representing the pausing index. Lines illustrate rightward shift of pausing ratio at all genes with cyclin D1 in their promoter (–5 kb, TSS) after cyclin D1 induction in JVM13-ctrl and JVM13-D1T286A cells. ***P < 2 × 10–16, Kolmogorov-Smirnov test. (E) Proportion of Pol II (IIo) and Pol (IIa) forms in primary MCL cases. P = 0.01, nonparametric Mann-Whitney U test. (F) Pol II (8WG16) antibody signal in primary MCL cases. P = 0.03, nonparametric Mann-Whitney U test. (G) Coimmunoprecipitation experiment in Z-138 cells using antibodies against cyclin D1 and control IgG. Immunoprecipitated proteins were analyzed by Western blot by blotting with cyclin D1 and Pol II antibody. Input at 1% was loaded as a control. (H) Coimmunoprecipitation experiment in HEK-293T-CDK9-FLAG-D1T286A cells with anti-FLAG resins. Immunoprecipitated proteins were analyzed by Western blot by blotting with CDK9 and cyclin D1 antibodies. HEK-293T–D1T286A immunoprecipitation was used as negative control.
Figure 7
Figure 7. Cyclin D1 overexpression renders tumor cells sensitive to CDK9 inhibitors.
(A) Cell survival of cyclin D1 inducible model after treatment with a CDK9 inhibitor (DBR) at increasing concentrations. JVM13-Ctrl and JVM13-D1T286A cells, after 24 hours of doxycylcine induction, were treated during 48 hours with DBR. Results are shown as mean ± SEM with respect to untreated cells (mean ± SEM, n = 3). *P < 0.05, Student’s t test. (B) MCL cell lines were treated during 72 hours with DBR. Results are shown with respect to untreated cells (mean ± SEM, n = 4). *P < 0.05, **P < 0.01, ***P < 0.001, Student’s t test. (C and D) Cell survival of MCL cell lines (C) and MM cell lines (D) after treatment with triptolide at 40 nM. Exponentially growing cell lines were treated, and cell survival was measured at 48 hours. Results are shown with respect to untreated (mean ± SEM, n = 4). *P < 0.05, **P < 0.01, ***P < 0.001, Student’s t test (C) or mixed-effects models (D). (E and F) Cell survival of 9 primary MCL cases after treatment with 60 μM DBR (E) or 200 nM triptolide (F). Cell survival was measured at 72 hours after treatment. Survival was calculated with respect to untreated controls, and the means of duplicate experiments for each group are represented. Nonparametric Mann-Whitney U test was applied. Holm-Bonferroni correction for multiple comparisons was applied to AC.
Figure 8
Figure 8. Proposed model for cyclin D1–dependent global transcriptional downregulation, a side effect of its oncogenic overexpression.
(A) Cyclin D1 canonical cell cycle role in normal cells. (B) Overexpression of cyclin D1, in addition to cell cycle induction, may interact with CDK9 and interfere with the normal release of paused Pol II, compromising active elongation. This would lead to a global transcription downmodulation, including of TSGs. The dotted lines indicate other potential oncogenic effects, such increased genomic instability due to augmented conflicts between transcription and DNA replication machinery.

Similar articles

See all similar articles

Cited by 5 articles

Publication types

Feedback