Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2014 Aug 1.
Published in final edited form as: Cancer Res. 2013 Jun 20;73(15):10.1158/0008-5472.CAN-12-3504. doi: 10.1158/0008-5472.CAN-12-3504

Transcription poisoning by topoisomerase I is controlled by gene length, splice sites and miR-142-3p

Stéphanie Solier 1, Michael C Ryan 1,3, Scott E Martin 2, Sudhir Varma 1, Kurt W Kohn 1, Hongfang Liu 1, Barry R Zeeberg 1, Yves Pommier 1,*
PMCID: PMC3874869  NIHMSID: NIHMS494533  PMID: 23786772

Abstract

Topoisomerase I (Top1) relaxes DNA supercoiling by forming transient cleavage complexes (Top1cc) up- and down-stream of transcription complexes. Top1cc can be trapped by carcinogenic and endogenous DNA lesions and by camptothecins resulting in transcription blocks. Here, we undertook genome-wide analysis of camptothecin-treated cells at exon resolution. RNA samples from HCT116 and MCF7 cells were analyzed with the Affy Exon array platform, allowing high resolution mapping along 18,537 genes. Long genes that are highly expressed were the most susceptible to down-regulation, whereas short genes were preferentially up-regulated. Along the body of genes, down-regulation was most important toward the 3’-end, and increased with the number of exon-intron junctions. Ubiquitin and RNA degradation-related pathway genes were selectively down-regulated. Parallel analysis of microRNA with the Agilent miRNA microarray platform revealed that miR-142-3p was highly induced by camptothecin. More than 10% of the down-regulated genes were targets of this p53-dependent microRNA. Our study demonstrates the profound impact of Top1cc on transcription elongation, especially at exon-intron junctions and on transcript stability by microRNA miR-142-3p up-regulation.

Keywords: Camptothecin, miRNA, RNA degradation, Topoisomerase I, ubiquitin

Introduction

DNA topoisomerase I (Top1) is essential in higher eukaryotes. It is required to relax DNA supercoiling generated by transcription, replication and chromatin remodeling (1). Top1 relaxes both positive and negative supercoiling by producing transient Top1 cleavage complexes (Top1cc), which are Top1-linked DNA single-strand breaks (1). Top1cc, which are normally very transient are the target of camptothecin (CPT) and its clinically used anticancer derivatives, topotecan, irinotecan and belotecan (1, 2). The drugs bind at the enzyme-DNA interface and block DNA religation, thereby leading to the trapping of Top1cc (1, 3). The trapped Top1cc are converted into lethal DNA lesions when their slow religation interferes with the progression of transcription and replication complexes (4, 5), leading to collisions that generate irreversible DNA breaks (1, 6–8). Those breaks activate multiple responses including cell cycle checkpoints, specific transcription factors, S and G2 arrest and ultimately cell death (9). Trapping of Top1cc can also be generated in the absence of drug treatment when the DNA template contains endogenous and exogenous DNA damage such as abasic sites, mismatches, oxidized bases, abasic sites, carcinogenic adducts and nicks (10, 11).

Beside Top1’s DNA untwisting activity, which is critical during transcription elongation for the dissipation of transcription-dependent supercoiling (12, 13), Top1 is closely linked to transcription in at least two other ways: at promoters, it can act as transcription regulator and at exon-intron junctions as splicing cofactor. Indeed, Top1 has been identified as a cofactor for activator-dependent transcription initiation by RNA polymerase II (Pol II) (14) and for the formation of active TFIID-TFIIA complexes (15). This promoter activity is independent of Top1’s unwinding activity. Top1’s implication in splicing stems from the initial discovery that it can phosphorylate SR splicing proteins (16) and more recent studies showing that Top1 can shift from its classical DNA untwisting activity to kinase activity after binding the SR-splicing factors (17). Moreover, trapping of Top1cc by CPT and Top1 inactivation impact RNA splicing (18–22) and generate alternative transcripts (23). Recently, CPT treatment has also been shown to preferentially alter the splicing of splicing-related factors, such as RBM8A, generating transcripts coding for inactive proteins lacking key functional domains (24), as well as to reactivate the transcription of the ubiquitin protein ligase, UBE3A, which is implicated in Angelman syndrome (25).

Here, we report the genome-wide effects of CPT on gene expression using the Exon Array platform that allowed a high-resolution mapping of transcripts for 18,537 individual genes. We used the same treatment protocol recently reported to map the impacts of CPT on genome-wide splicing (24).

Materials and Methods

Differential analysis using R package LIMMA

We used two main steps for analysis: i) differential microarray analysis and ii) the analysis of the probe set genome location for top differentially expressed probe sets. The microarray analysis was conducted packages available in R Bioconductor (versions available in June 2011). We first normalized the data using the RMA package (26). We defined three groups (late – 15 and 20 h, early – 1, 2, and 4 h, and control – 4 and 20 h) and conducted differential analysis between groups using linear models and empirical Bayes methods provided by LIMMA (Linear Models for Microarray Data) package (27). The mapping of the probes to the human genome (37.1) was obtained using Bowtie (28) and probe sets with all member probes uniquely mapped to the genome were kept. The distance of a probe set to 5’ end of the corresponding gene was obtained by using the genome start position of the probe set and the position of the 5’ end of the gene was obtained from Entrez Gene. The distance was normalized to a value between 0 and 1. The distribution of the normalized distances for the top 10000 differentially expressed (up-regulated or down-regulated) probe sets was obtained using density plot.

Chemicals and cells

Camptothecin was obtained from Sigma-Aldrich (St. Louis, MO). Human HCT116, MCF7 and MDA-MB-231 cell lines were obtained from ATCC (Rockville, MD) and grown in DMEM (Invitrogen, Carlsbad, CA) (HCT116, MCF7) or RPMI (Invitrogen) (MDA-MB-231) supplemented with 10% fetal bovine serum (FBS) (Gemini Bio-products, West Sacramento, CA) at 37°C in 95% air and 5% CO2. p53+/+ and p53−/− HCT116 cells were kind gifts from Bert Vogelstein (Johns Hopkins Oncology Center, Baltimore, MD) (29).

miRNA inhibitor or miRNA mimic transfection

142-3p inhibitor, 142-3p mimic, miScript inhibitor negative control and negative control siRNA were from Qiagen (Valencia, CA) (catalogue numbers MIN0000434, MSY0000434, 1027271 and 1027310). Negative control siRNA was used as a control miRNA mimic. Cells were seeded at the density of 400,000 cells per well in 2.3 mL of medium containing serum, shortly before transfection. For each sample, 400 pmol of miRNA inhibitor (or 40 pmol of miRNA mimic) were mixed with 100 µL culture medium without serum (mixture A). Then 12 µL of HiPerfect (Qiagen) was added to mixture A and incubated 10 min at room temperature to allow the formation of transfection complexes. Finally the complexes were added to the cells, after 24 h the medium was replaced with regular medium and the cells were incubated for a further 24h.

Short interfering RNA (siRNA)

For Top1 down-regulation, cells were transfected with siRNA duplex (Qiagen, Valencia, CA) against the sequence AAGGACTCCATCAGATACTAT from the Top1 mRNA. A negative control siRNA duplex was obtained from Qiagen (target DNA sequence: AATTCTCCGAACGTGTCACGT). Cells were seeded in 6-well plates, at a density of 150,000 cells per well 16 h before transfection (31). BCL2L1 siRNAs were obtained from Qiagen (SI03025141) and Ambion (Grand Island, NY) (s1921). MAP3K7IP2 siRNAs were obtained from Qiagen (SI03107685) and Ambion (s23074). For BCL2L1 and MAP3K7IP2 siRNAs, transfections were performed in 384-well plates. Briefly, 20 µL of serum-free media containing Lipofectamine RNAiMax (Life Technologies, Grand Island, NY) (0.05 µL) was added to wells containing siRNA (0.8 pmol). Lipid and siRNA were allowed to complex for 45 min at ambient temperature before addition of 600 cells in RPMI-20% FBS to yield final transfection mixtures containing 20 nM siRNA in RPMI-10% FBS. CPT (0.1% DMSO) was added 48 h post-transfection and viability was assayed 72 h later.

RT-PCR

Cells were washed in PBS. RNA extraction was performed with the “Nucleospin RNA II” kit (Macherey-Nagel, Bethlehem, PA). The “OneStep RT-PCR” kit (Qiagen) was used as previously described (24). Primer sequences are listed in Supplementary Table 1.

Q-RT-PCR

Cells were washed in PBS. RNA extraction was performed with the “Nucleospin RNA II” kit (Macherey-Nagel). Real-time RT-PCR was performed with the QuantiFast® SYBR® Green RT-PCR kit (Qiagen) on the ABI 7900 thermocycler (Applied Biosystems, Carlsbad, CA). Expression level of gene of interest was normalized by β2 microglobulin RNA level of the same sample. Reaction mixtures contained 5 µl of 2× QuantiFast SYBR Green RT-PCR Master Mix, 0.5 µl of QuantiFast RT Mix and 50 ng of template RNA in a final volume of 10 µl containing primers (Proligo, Paris, France) at 1 µM. Relative gene expression was expressed as 2−ΔΔCt (ΔΔCt=ΔCtsample−ΔCtcalibrator, ΔCt=Ctgene−Ctβ2 microglobulin). Primer sequences are listed in Supplementary Table S1.

Q-PCR for miRNA

Cells were washed in PBS. RNA extraction was performed with the “mirVana miRNA isolation kit (Ambion). An aliquot of 1 µg of RNA was reverse transcribed using the miScript Reverse Transcription Kit (Qiagen). Real-time PCR was performed with miScript SYBR® Green PCR kit (Qiagen) on the ABI 7900 thermocycler (Applied Biosystems). The expression level of hsa-miR-142-3p was normalized by the RNU1A RNA level of the same sample. Reaction mixtures contained 10 µL of 2× Quantitect SYBR Green PCR Master Mix and 1 µL of reverse-transcriptase-generated cDNA in a final volume of 20 µL containing miScript Universal Primer and miScript Primer Assay (Qiagen). Relative gene expression was expressed as 2−ΔΔCt (ΔΔCt=ΔCtsample−ΔCtcalibrator, ΔCt=CtmiR-142-3p−CtRNU1A).

Cell viability

Cell viability was measured using CellTiter Glo (Promega, Madison, WI). Briefly, 25 µL of reagent was added to sample wells and incubated for 15 min at ambient temperature prior to reading luminescence using a PerkinElmer Envision Plate Reader (model 2104).

Results

Global gene expression alterations induced by Top1cc

The Affy Exon array (GeneChip Human Exon 1.0 ST array) allows in depth analysis of 18,537 genes with approximately 4 probes per exon and an average of 40 probes per gene. It can also be used to determine overall “gene-level” expression by averaging multiple probes on different exons. We purified total RNA from human colon carcinoma HCT116 cells treated with CPT for 1, 2, 4, 15 and 20 hours (Fig. 1A), and performed Exon array analysis for each sample. Controls samples treated with DMSO (0.1%; the solvent used to dissolve CPT) were analyzed at 4 and 20 h (Fig. 1A). Non-responsive probes were removed and only genes with 10 or more remaining probes were included in the analysis (all the data are in GEO; accession number GSE37352; http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37352).

Figure 1.

Figure 1

Open in a new tab

Experimental protocol and global impact of Top1 poisoning. A, Experimental design. Cells were treated with 10 µM CPT for 1, 2, 4, 15 and 20 h. Control samples received vehicle alone (0.1% DMSO, 4 and 20 h). B, Down-regulated and up-regulated genes (2-fold or more) in CPT-treated HCT116 cells. C, Down-regulated and up-regulated genes (2-fold or more) in CPT-treated MCF7 cells.

Overall 3808 genes (20% of all genes) were down-regulated (by 2-fold or more) after CPT treatment while 835 genes (5% of all genes) were up-regulated (by 2-fold or more) (Fig. 1B) (see Supplemental Tables 2 and 3). At early time treatments (1, 2 and 4 h), only few genes were differentially expressed; it was mostly at late times (15, 20 h) that transcripts were up- or down-regulated (Fig. 1B).

Top1cc preferentially down-regulates highly expressed genes and up-regulates genes with low expression

First, we analyzed whether differential gene expression responses were linked to the basal expression level of the genes. Basal expression for a given gene was determined as the average between the expression values for the 2 controls (4 h and 20 h DMSO treatment). After ranking all the genes in the array in term of transcript expression under baseline untreated conditions, we divided them in 10 groups of equal size based on their basal expression level. We then looked at the number of down- (Fig. 2A) and up-regulated genes (Fig. 2B) in each group. The most highly expressed genes were the most down-regulated both at early times (1, 2 and 4 h CPT treatment) and at late times (15 and 20 h treatment) (Fig. 2A). By contrast, the up-regulated genes both at early and late times were those with lowest expression prior to CPT treatment (Fig. 2B).

Figure 2.

Figure 2

Open in a new tab

Effect of Top1 poisoning on gene expression is correlated with the basal gene expression level. A, Down-regulation of highly expressed genes by CPT. The basal expression level for a specific gene corresponded to the average between the log2 expression value for the controls (4 and 20 h DMSO treatment). The genes in the array were ranked according to transcripts levels. Genes were divided in 10 groups of same size depending of their basal expression level (~1745 genes by group). Then we determined the proportion of down-regulated genes in each group. The x-axis corresponds to the average basal expression level for each of the ten groups of genes. The y-axis corresponds to the number of down-regulated genes in each group. Upper panel: plot for the 217 “early” down-regulated genes (down-regulated within 4 h of CPT exposure). Lower panel: plot for the 3759 “late” down-regulated genes (down-regulated after CPT 15 h and/or 20 h). Correlation coefficients (r) are indicated. B, Up-regulation of low expression genes. The basal expression level for a specific gene corresponded to the average between the log2 expression values for the controls (4 and 20 h DMSO treatment). The genes in the array were ranked according to transcripts levels. Genes were divided in 10 groups of same size depending of their basal expression level (~1745 genes by group). Then we determined the proportion of up-regulated genes in each group. The×axis corresponds to the average basal expression level for each of the ten groups of genes. The y axis corresponds to the number of up-regulated genes in each group. Upper panel: plot for the 131 “early” up-regulated genes (up-regulated within 4 h of CPT exposure). Lower panel: plot for the 810 “late” up-regulated genes (up-regulated after CPT 15 h and/or 20 h). Correlation coefficients (r) are indicated.

Impact of gene length on transcriptional down- and up-regulation

Next, we analyzed the impact of gene length on Top1cc-induced up- and down-regulation. Figure 3A shows that the gene density curve for the down-regulated genes is shifted to the right compared to the gene density curve for all the genes, meaning that the down-regulated genes were the longest genes. On the other hand, the gene density curve for the up-regulated genes was shifted to the left, meaning that the up-regulated genes were the shortest (Fig. 3A). The median gene length for the overall genome [~24 kilobases (kb)] increased to ~66 kb for the down-regulated genes and decreased to ~7 kb for the up-regulated genes (Fig. 3B). These results demonstrate that the long genes are selectively down-regulated while the short genes are selectively up-regulated.

Figure 3.

Figure 3

Open in a new tab

Effect of Top1 poisoning on gene expression is correlated with gene length. A, Distribution of the down-regulated, up-regulated genes and all the genes of the array depending of their gene length. The down-regulated genes (3808), the up-regulated genes (835) and all the genes of the array (18537 genes) are represented in green, red and black, respectively. B, Median and average of gene length (in bases) for the down-regulated genes, the up-regulated genes and all the genes of the array.

Down-regulation is predominant at the 3’-end of genes

The exon-array platform allowed us to test whether the genes were down-regulated or up-regulated differentially along their length. For this purpose, we plotted the density of the 10,000 top differentially expressed probes (up- and down-regulated) and 10,000 random probes, depending of their distance from the 5’-end of the gene. Our analysis showed that the down-regulated probes were preferentially located at the 3’-end of genes compared to random probes, and this effect was accentuated at late time treatments (Fig. 4A, B, left panels).

Figure 4.

Figure 4

Open in a new tab

Transcripts are differentially affected along the length of genes. A, Distribution of the top 10000 differentially expressed probes (5770 down-regulated probes in green, 4230 up-regulated probes in red) at early times treatment with CPT depending of their distance from the 5’ end of the gene. The distribution of the 10,000 random probes is represented in black. The differential analysis was done with R package LIMMA (see materials and methods). B, Distribution of the top 10,000 differentially expressed probes (9233 down-regulated probes in green, 767 up-regulated probes in red) at late times treatment with CPT depending of their distance from the 5’ end of the gene. The distribution of 10,000 random probes is represented in black. The differential analysis was done with R package LIMMA (see materials and methods).

Conversely, for the up-regulated genes, the probes that were up-regulated had a preference for the 5’-end of the genes at early times. However, they had the same location than random probes at late times, but (Fig. 4A, B, right panels). To test whether the observed increase at 5’ ends of genes (Figure 4A, right panel) may influence the tendency of short genes to be up-regulated by CPT, we took the 131 early up-regulated genes, separated them in two groups based of their length (short genes: less than 24566 bases, long genes: 24566 bases or more), and looked at the up-regulation mean of their 5’ probes (probes located in the first 1000 bases of the gene) compared to the up-regulation mean of the other probes (probes located after the first 1000 bases of the gene). For the early short up-regulated genes, we obtained the same up-regulation for both the 5’ probes and the other probes: 1.63 and 1.64 respectively, indicating that CPT-induced gene upregulation is not solely based on accumulation of 5’ probes for short genes.

Both gene length and exon-intron junctions are related to transcripts down-regulation

Figure 5A shows that the probability for a gene to be down-regulated increased with the number of exons. Conversely, intronless genes or genes with few exons had a higher probability to be up-regulated (Fig. 5A, B). The median number of exons is 9 for the overall genome of HCT-116 cells, while it was 14 for the down-regulated genes and 4 for the up-regulated genes. Thus, down-regulated transcripts originated from long genes with a high number of exons.

Figure 5.

Figure 5

Open in a new tab

Preferential down-regulation of genes with numerous exon-intron junctions. A, The x-axis corresponds to the number of exons. The y-axis corresponds to the proportion of down-regulated genes (in green) or up-regulated genes (in red) and unchanged genes (in black) among the genes with a determined number of exons. B, Intronless genes have a higher probability to be up-regulated than down-regulated. Proportion of down-regulated genes, up-regulated genes and unchanged genes in intronless genes or in introns-containing genes are represented in green, red and black, respectively. C, Same length genes have a higher probability to be down-regulated when they have large number of exons. The number of down-regulated genes for two groups of same size genes, that differ only by the number of exons (a group with lower number of exons, a second group with a larger number of exons) is plotted in green. D, Same exons number genes have a higher probability to be down-regulated if they are long. The number of down-regulated genes for two groups of genes with the same exons number, but differing by gene length (a group with short genes, a second group with long genes) is plotted in green.

Because long genes also contain high number of exons, we analyzed the importance of exon-intron junctions. To eliminate the gene length parameter, we divided genes in groups of same size that differed only by the number of exons (Fig. 5C). By applying this correction, we observed that down-regulation increases when genes have more exons. This finding is not limited to the two specific groups shown in Figure 5C; it is general for different groups of genes with matched lengths (Supplementary Fig. 1).

In addition, to examine gene length and eliminate the exons number parameter, we compared genes with same exons number but different length (Fig. 5D). The proportion of down-regulated genes was higher in long genes. Together these data demonstrate that both the length of a gene and the frequency of exon-intron junctions per gene are linked to its down-regulation.

Preferential down-regulation of the “RNA degradation” and “ubiquitin-mediated proteolysis” genes

Gene ontology analyses of the 3808 genes up-regulated by CPT in HCT116 cells showed significant correlations for the oxidative phosphorylation, ribosome and p53 signaling pathways (Supplementary Fig. 2A). We also analyzed by Affy Exon array the response of the breast carcinoma MCF7 cells treated with CPT (Fig. 1C) (all the data are in GEO; accession number GSE37352; http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37352). One hundred genes were up-regulated in MCF7 cells. They correspond to the p53 signaling pathway, MAPK signaling pathway and cell cycle-related genes. These results confirm that the p53 signaling pathway is transcriptionally up-regulated in both HCT116 and MCF7 cells (Supplementary Fig. 2), which is consistent with the p53 status of those two cell lines (30).

Among the most significant GO categories for the genes down-regulated after CPT treatment in HCT116 cells are the “RNA degradation” and “ubiquitin-mediated proteolysis”, two categories that are also present for the genes that are down-regulated in MCF7 cells (Supplementary Table 4). Supplementary Figure 3A shows the large number of genes implicated in RNA decay, and Supplementary Figure 3B demonstrates that the CNOT genes are mainly down-regulated at late time treatments. We confirmed the Affy Exon array data by RT-PCR experiments (Supplementary Fig. 3C). It is noteworthy that down-regulation was detectable at 2 h by Q-RT-PCR (Supplementary Fig. 3D). Similarly, the down-regulation of “ubiquitin-mediated proteolysis” genes observed with the Affy Exon array (Supplementary Fig. 4A, B) was confirmed by RT-PCR and Q-RT-PCR (Supplementary Fig. 4C & 4D).

To differentiate whether the transcriptional effects of CPT were related to Top1cc or to Top1 inactivation, we compared the effects of Top1 down-regulation by siRNA vs. CPT treatment with whole genome transcript analyses by Affy Exon Array (the data are in GEO ; accession number GSE37352). By contrast to CPT, down-regulation of Top1 had only limited impact on gene expression. Only few genes were up (DDIT4L) or down-regulated (SLC16A6, CPA4) following Top1 down-regulation, indicating that the generation of Top1cc rather than Top1 inactivation is responsible for inducing differential gene expression. Moreover, contrary to CPT treatment, Top1 silencing did not down-regulate the ubiquitin-related genes, CUL5 and UBE2W (see Supplementary Fig. 5). These results indicate the importance of Top1cc for the transcriptional effects of CPT.

p53-dependent miR-142-3p up-regulation as a novel mechanism of genomic down-regulation and CPT-induced cell death

In parallel to the Affy Exon array, we performed Agilent miRNA microarray (8×15K version 3) analyses in human colon carcinoma HCT116 cells treated with CPT for 1, 2, 4, 15 and 20 hours (see Fig. 1) (the data are in GEO; accession number GSE37358; http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37358). Only few miRNA were affected by CPT treatment. miR-142-3p was among the most significantly up-regulated miRNAs (Fig. 6A–B). This miRNA has 1329 predicted target genes including the “ubiquitin-mediated proteolysis” gene family. Moreover, 415 of these predicted target genes were down-regulated by CPT treatment (Chi-Square with Yates correction, P value < 0.0001). We confirmed by PCR that CUL5 and UBE2W, two of the predicted target genes from “ubiquitin-mediated proteolysis” gene family, were effectively down-regulated by miR-142-3p mimic (Fig. 6C) and CPT treatment (Supplementary Fig. 4C and Fig. 6D). To confirm that the down-regulation of CUL5 and UBE2W after CPT treatment was due to the up-regulation of miR-142-3p, we used an inhibitor of miR-142-3p in our experiments. As expected, the miR-142-3p inhibitor strongly counteracted the down-regulation of CUL5 and UBE2W (Fig. 6D). Because the 5’ flanking region of miR-142-3p contains a p53 binding sequence (31) (Fig. 6E), we tested whether p53 was functionally related to miR-142-3p up-regulation. For this purpose, we compared p53+/+ or p53−/− HCT116 cells. The up-regulation of mir-142-3p was delayed in p53−/− cells compared to p53+/+ cells (Fig. 6F). Altogether, these results demonstrated that p53-dependent miR-142-3p up-regulation could be involved in the down-regulation of 10% of the down-regulated genes after CPT treatment.

Figure 6.

Figure 6

Open in a new tab

miR-142-3p up-regulation is responsible for gene down-regulation in response to CPT treatment. A, Agilent miRNA microarray data for hsa-miR-142-3p expression after CPT treatment. The log2 difference for hsa-miR-142-3p expression depending on CPT treatment normalized to the untreated controls. B, hsa-miR-142-3p up-regulation after CPT treatment. hsa-miR-142-3p was analyzed by Q-PCR. C, hsa-miR-142-3p mimic down-regulates CUL5 and UBE2W transcripts. HCT116 cells transfected with hsa-miR-142-3p mimic or negative control were treated with CPT (10 µM, 15 h or 20 h). CUL5 and UBE2W transcripts were analyzed by Q-RT-PCR. The y-axis corresponds to the fold decrease of CUL5 (black bar) or UBE2W (gray bar) after CPT treatment (15 h or 20 h) compared to control. D, The hsa-miR-142-3p inhibitor counteracts the down-regulation of CUL5 and UBE2W transcripts. HCT116 cells transfected with the hsa-miR-142-3p inhibitor or miScript inhibitor negative control were treated with CPT (10 µM, 15 h or 20 h). CUL5 and UBE2W transcripts were analyzed by Q-RT-PCR. The y-axis corresponds to the fold decrease of CUL5 (left panel) or UBE2W (right panel) after CPT treatment (15 h or 20 h) compared to control. The black bars correspond to inhibitor negative control, and the gray bars correspond to hsa-miR-142-3p inhibitor. E, Schematic representation of hsa-miR-142 and its flanking region showing its p53 binding site. F, miR-142-3p up-regulation after CPT treatment is p53 dependent. p53+/+ and p53−/− HCT116 cells were treated with CPT (10 µM, 1 h, 2 h, 4 h, 15 h or 20 h).. hsa-miR-142-3p was analyzed by Q-PCR. p53+/+ HCT116 cells are in black and p53−/− HCT116 cells are in gray.

As for CPT, the surexpression of miR-142-3p was cytotoxic (Supplementary Fig. 6A). Moreover, down-regulation by siRNA of BCL2L1 and MAP3K7IP2, two genes that are down-regulated after CPT treatment and are targeted by miR142-3p, increased CPT sensitivity by 25-fold (Supplementary Fig. 6B and C). Altogether, our data demonstrate the functional impact of miR-142-3p on cell toxicity and down-regulation of critical survival genes.

Discussion

Our study demonstrates the profound impact of Top1cc on transcription. Using a combination of high-resolution genome-wide analyses and targeted gene experiments, we show that the specific Top1cc poison CPT down-regulates (by at least 2-fold) more than one fifth of the genome. We demonstrate that this inhibition can be explained by the combination of three mechanisms: i) Top1cc-mediated transcription arrest along the body of genes with incremental effect toward the 3’-end of long genes, ii) presence of high number of exon-intron junctions that tend to arrest transcript elongation, and iii) up-regulation of at least one specific miRNA, miR-142-3p that downregulates a large number of survival genes.

miRNAs target the 3’ untranslated region (3’UTR) of genes (32). Perfect or near-perfect base pairing with the target RNA promotes cleavage of the RNA (33). miRNA that are partially complementary to their target genes can also promote their deadenylation, causing mRNA destabilization and degradation possibly starting at the 3’-end of the transcripts (34). Up-regulation of miR-142-3p can be invoked for the down-regulation of more than 400 of the total 3800 genes down-regulated by CPT including genes coding for ubiquitin pathways, and which respond in a coordinated manner to CPT treatment. Although other miRNA were known to be up-regulated by DNA damage responses [miR-34a (35, 36), miR-34c (37), miR-192, miR-215 (38, 39), miR-16-1, miR-143 and miR-145 (40)], in our analysis, only miR-142-3p was overexpressed significantly after CPT treatment. Based on the presence of a p53 binding sequence upstream from the miR-142-3p and the impact of p53 knockout on miR-142-3p induction after CPT treatment, we conclude that miR-142-3p is a novel p53-dependent miRNA. It is also likely that miR-142-3p is up-regulated by DNA damage independently of p53 since it has been recently reported to be up-regulated by ionizing radiation in M059K glioblastoma cells, TK6 human B lymphoblast cells and Jurkat lymphoblast acute T-cell leukemia cells (41, 42), which are p53-defective.

At least two other mechanisms (besides miR-142-3p discussed above) are involved in the Top1cc-induced transcription inactivation. First, our Exon array analyses demonstrate an overall presence of aborted transcripts in CPT-treated cells. This result is consistent with earlier studies by Kann and Kohn who reported that CPT shifts pulse-labeled RNA to lower molecular weight (43). More recently, using 3H-uridine pulse-labeling, Ljungman and Hanawalt also showed that CPT inhibits elongation by Pol II in the dihydrofolate reductase gene (4). Our genome-wide analyses generalize these earlier results by demonstrating preferential truncation of long transcripts. In addition, we show that gene up-regulation was confined to short transcripts. Aborted transcripts produced by premature chain termination may have profound cellular effects. The simplest mechanism for the formation of abortive transcripts in CPT-treated cells is Pol II arrest at trapped Top1cc. Pol II is rapidly arrested by Top1cc (4, 44) with reduction of Pol II density at promoter pausing sites (45), activation of low abundance antisense RNA (23) and rapid hyperphosphorylation of Pol II (46). The role of Top1cc rather than Top1 inactivation by sequestration at Top1cc is supported by the fact that Top1 siRNA had a much weaker effect than CPT treatment (see Supplemental Fig. 5).

Our study reveals another mechanism related to exon-intron junctions for the generation of aborted transcript. Indeed, transcript down-regulation was significantly correlated to the number of exon-intron junctions in CPT-treated cells (see Fig. 5C and Supplementary Fig. 1). Thus, it appears that the trapping of Top1cc might be particularly deleterious on exon-intron junctions, which is plausible based on the known involvement of Top1 in RNA splicing. The splicing alterations generated by CPT could produce premature stops and generate instable transcripts subjected to nonsense-mediated mRNA decay (NMD) (21, 24, 47). Recently, an ExonHit Array on the RNA samples used in the present study (see protocol in Fig. 1A) showed that CPT preferentially affected the splicing of splicing-related factors. The preferential effect of CPT on genes encoding splicing factors may explain the abnormal splicing of a large number of genes in response to Top1cc (24), and raises the question as to whether transcript defects identified in the present study are related to splice defects. The alternatively spliced genes represent 9% of the down-regulated genes, 2% of the up-regulated genes and 5% of the unchanged genes. These percentages indicate that the down-regulated genes have a higher probability to be spliced than the unchanged genes and the up-regulated genes (Exact Fisher test, P value < 2.2e-16). Together, our results suggest the importance of CPT-induced Top1cc at or near intron-exon junctions for CPT-induced transcription stalling and splicing alterations.

We found several pathways significantly down-regulated in CPT-treated cells: ubiquitin-mediated proteolysis, cell cycle, RNA degradation, basal transcription factors, TGFβ and immune response signaling (see Supplementary Table 4). Down-regulation of cell cycle-related genes leading to growth arrest is a well-known effect of Top1cc (48). The sensitivity of RNA-related genes has recently been noted by Capranico et al. (49). Unexpectedly, the most significantly enriched gene category in our study is “ubiquitin-mediated proteolysis”. Down-regulation of ubiquitin pathways is unexpected since ubiquitylation is a major component of the DNA damage response and a prerequisite to Top1cc repair (11). Overexpression of CUL3, a component of an SCF (Skip1-Cul-F-Box) E3 ligase, has been demonstrated to increase Top1 ubiquitylation and subsequent degradation resulting in CPT resistance (50). However, in our study, CUL3 and many other ubiquitin genes are down-regulated, consequently the DNA breaks are not repaired, contributing to the cytotoxicity of Top1cc.

In summary, our exon-specific gene expression study of the whole human genome in response to Top1cc provides novel insights in the transcriptional effects of Top1cc. At least three mechanisms appear to coexist to inactivate gene expression in CPT-treated cancer cells: miRNA up-regulation by DNA damage responses, transcript elongation arrests by trapped Top1cc, and exon-intron junction interference by Top1cc. Moreover, cells that are targeted by CPT appear to selectively inactivate several key pathways involved at least in ubiquitin metabolism, cell cycle and RNA stability and to upregulate miR-142-3p, which downregulates survival genes. These findings are relevant not only for basic cell biology since Top1cc form in response to endogenous and exogenous DNA lesions but also for cancer therapeutics as Top1cc-targeted drugs are routinely used for various human cancers.

Supplementary Material

1

Acknowledgements

Our studies were supported by the Center for Cancer Research, the Intramural program of the National Cancer Institute, NIH. We wish to thank Dr. Bert Vogelstein (Johns Hopkins University) for providing the HCT116 p53 +/+ and −/− cell lines. We also wish to thank Dr. Natasha Caplen, Center for Cancer Research, NCI, for insightful discussion regarding our microRNA analyses.

Footnotes

No conflict of interest

References

  • 1.Pommier Y. Topoisomerase I inhibitors: camptothecins and beyond. Nat Rev Cancer. 2006;6:789–802. doi: 10.1038/nrc1977. [DOI] [PubMed] [Google Scholar]
  • 2.Pommier Y. DNA topoisomerase I inhibitors: chemistry, biology, and interfacial inhibition. Chem Rev. 2009;109:2894–2902. doi: 10.1021/cr900097c. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Hsiang YH, Hertzberg R, Hecht S, Liu LF. Camptothecin induces protein-linked DNA breaks via mammalian DNA topoisomerase I. J Biol Chem. 1985;260:14873–14878. [PubMed] [Google Scholar]
  • 4.Ljungman M, Hanawalt PC. The anti-cancer drug camptothecin inhibits elongation but stimulates initiation of RNA polymerase II transcription. Carcinogenesis. 1996;17:31–35. doi: 10.1093/carcin/17.1.31. [DOI] [PubMed] [Google Scholar]
  • 5.Seiler JA, Conti C, Syed A, Aladjem MI, Pommier Y. The Intra-S-Phase Checkpoint Affects both DNA Replication Initiation and Elongation: Single-Cell and - DNA Fiber Analyses. Mol Cell Biol. 2007;27:5806–5818. doi: 10.1128/MCB.02278-06. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Hsiang Y-H, Lihou MG, Liu LF. Arrest of DNA replication by drug-stabilized topoisomerase I-DNA cleavable complexes as a mechanism of cell killing by camptothecin. Cancer Res. 1989;49:5077–5082. [PubMed] [Google Scholar]
  • 7.Strumberg D, Pilon AA, Smith M, Hickey R, Malkas L, Pommier Y. Conversion of topoisomerase I cleavage complexes on the leading strand of ribosomal DNA into 5'-phosphorylated DNA double-strand breaks by replication runoff. Mol Cell Biol. 2000;20:3977–3987. doi: 10.1128/mcb.20.11.3977-3987.2000. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Wu J, Liu LF. Processing of topoisomerase I cleavable complexes into DNA damage by transcription. Nucleic Acids Res. 1997;25:4181–4186. doi: 10.1093/nar/25.21.4181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Pommier Y. DNA Topoisomerases and Cancer. New York: Springer-Cancer Research; 2012. [Google Scholar]
  • 10.Dexheimer TS, Kozekova A, Rizzo CJ, Stone MP, Pommier Y. The modulation of topoisomerase I-mediated DNA cleavage and the induction of DNA-topoisomerase I crosslinks by crotonaldehyde-derived DNA adducts. Nucleic Acids Res. 2008;36:4128–4136. doi: 10.1093/nar/gkn334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Pommier Y, Barcelo JM, Rao VA, Sordet O, Jobson AG, Thibaut L, et al. Repair of topoisomerase I-mediated DNA damage. Prog Nucleic Acid Res Mol Biol. 2006;81:179–229. doi: 10.1016/S0079-6603(06)81005-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Champoux JJ. DNA topoisomerases: structure, function, and mechanism. Annu Rev Biochem. 2001;70:369–413. doi: 10.1146/annurev.biochem.70.1.369. [DOI] [PubMed] [Google Scholar]
  • 13.Wang JC. Cellular roles of DNA topoisomerases: a molecular perspective. Nat Rev Mol Cell Biol. 2002;3:430–440. doi: 10.1038/nrm831. [DOI] [PubMed] [Google Scholar]
  • 14.Kretzschmar M, Meisterernst M, Roeder RG. Identification of human DNA topoisomerase I as a cofactor for activator-dependent transcription by RNA polymerase II. Proc Natl Acad Sci U S A. 1993;90:11508–11512. doi: 10.1073/pnas.90.24.11508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Shykind BM, Kim J, Stewart L, Champoux JJ, Sharp PA. Topoisomerase I enhances TFIID-TFIIA complex assembly during activation of transcription. Genes Dev. 1997;11:397–407. doi: 10.1101/gad.11.3.397. [DOI] [PubMed] [Google Scholar]
  • 16.Rossi F, Labourier E, Forne T, Divita G, Derancourt J, Riou JF, et al. Specific phosphorylation of SR proteins by mammalian DNA topoisomerase I. Nature. 1996;381:80–82. doi: 10.1038/381080a0. [DOI] [PubMed] [Google Scholar]
  • 17.Andersen FF, Tange TO, Sinnathamby T, Olesen JR, Andersen KE, Westergaard O, et al. The RNA splicing factor ASF/SF2 inhibits human topoisomerase I mediated DNA relaxation. J Mol Biol. 2002;322:677–686. doi: 10.1016/s0022-2836(02)00815-x. [DOI] [PubMed] [Google Scholar]
  • 18.Shkreta L, Froehlich U, Paquet ER, Toutant J, Elela SA, Chabot B. Anticancer drugs affect the alternative splicing of Bcl-x and other human apoptotic genes. Mol Cancer Ther. 2008;7:1398–1409. doi: 10.1158/1535-7163.MCT-08-0192. [DOI] [PubMed] [Google Scholar]
  • 19.Eisenreich A, Bogdanov VY, Zakrzewicz A, Pries A, Antoniak S, Poller W, et al. Cdc2-like kinases and DNA topoisomerase I regulate alternative splicing of tissue factor in human endothelial cells. Circ Res. 2009;104:589–599. doi: 10.1161/CIRCRESAHA.108.183905. [DOI] [PubMed] [Google Scholar]
  • 20.Solier S, De Cian MC, Bettaieb A, Desoche L, Solary E, Corcos L. PKC zeta controls DNA topoisomerase-dependent human caspase-2 pre-mRNA splicing. FEBS Lett. 2008;582:372–378. doi: 10.1016/j.febslet.2007.12.032. [DOI] [PubMed] [Google Scholar]
  • 21.Solier S, Lansiaux A, Logette E, Wu J, Soret J, Tazi J, et al. Topoisomerase I and II inhibitors control caspase-2 pre-messenger RNA splicing in human cells. Mol Cancer Res. 2004;2:53–61. [PubMed] [Google Scholar]
  • 22.Soret J, Gabut M, Dupon C, Kohlhagen G, Stevenin J, Pommier Y, et al. Altered serine/arginine-rich protein phosphorylation and exonic enhancer-dependent splicing in Mammalian cells lacking topoisomerase I. Cancer Res. 2003;63:8203–8211. [PubMed] [Google Scholar]
  • 23.Baranello L, Bertozzi D, Fogli MV, Pommier Y, Capranico G. DNA topoisomerase I inhibition by camptothecin induces escape of RNA polymerase II from promoter-proximal pause site, antisense transcription and histone acetylation at the human HIF-1alpha gene locus. Nucleic Acids Res. 2010;38:159–171. doi: 10.1093/nar/gkp817. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Solier S, Barb J, Zeeberg BR, Varma S, Ryan MC, Kohn KW, et al. Genome-wide analysis of novel splice variants induced by topoisomerase I poisoning shows preferential occurrence in genes encoding splicing factors. Cancer Res. 2010;70:8055–8065. doi: 10.1158/0008-5472.CAN-10-2491. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Huang HS, Allen JA, Mabb AM, King IF, Miriyala J, Taylor-Blake B, et al. Topoisomerase inhibitors unsilence the dormant allele of Ube3a in neurons. Nature. 2011 doi: 10.1038/nature10726. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4:249–264. doi: 10.1093/biostatistics/4.2.249. [DOI] [PubMed] [Google Scholar]
  • 27.Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3 doi: 10.2202/1544-6115.1027. Article3. [DOI] [PubMed] [Google Scholar]
  • 28.Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25. doi: 10.1186/gb-2009-10-3-r25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Bunz F, Dutriaux A, Lengauer C, Waldman T, Zhou S, Brown JP, et al. Requirement for p53 and p21 to sustain G2 arrest after DNA damage. Science. 1998;282:1497–1501. doi: 10.1126/science.282.5393.1497. [DOI] [PubMed] [Google Scholar]
  • 30.Zoppoli G, Solier S, Reinhold WC, Liu H, Connelly JW, Jr, Monks A, et al. CHEK2 genomic and proteomic analyses reveal genetic inactivation or endogenous activation across the 60 cell lines of the US National Cancer Institute. Oncogene. 2012;31:403–418. doi: 10.1038/onc.2011.283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Sinha AU, Kaimal V, Chen J, Jegga AG. Dissecting microregulation of a master regulatory network. BMC Genomics. 2008;9:88. doi: 10.1186/1471-2164-9-88. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Zhang R, Su B. Small but influential: the role of microRNAs on gene regulatory network and 3'UTR evolution. J Genet Genomics. 2009;36:1–6. doi: 10.1016/S1673-8527(09)60001-1. [DOI] [PubMed] [Google Scholar]
  • 33.Kawasaki H, Taira K. MicroRNA-196 inhibits HOXB8 expression in myeloid differentiation of HL60 cells. Nucleic Acids Symp Ser (Oxf) 2004:211–212. doi: 10.1093/nass/48.1.211. [DOI] [PubMed] [Google Scholar]
  • 34.Eulalio A, Huntzinger E, Nishihara T, Rehwinkel J, Fauser M, Izaurralde E. Deadenylation is a widespread effect of miRNA regulation. RNA. 2009;15:21–32. doi: 10.1261/rna.1399509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.He L, He X, Lowe SW, Hannon GJ. microRNAs join the p53 network--another piece in the tumour-suppression puzzle. Nat Rev Cancer. 2007;7:819–822. doi: 10.1038/nrc2232. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.He L, He X, Lim LP, de Stanchina E, Xuan Z, Liang Y, et al. A microRNA component of the p53 tumour suppressor network. Nature. 2007;447:1130–1134. doi: 10.1038/nature05939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Cannell IG, Kong YW, Johnston SJ, Chen ML, Collins HM, Dobbyn HC, et al. p38 MAPK/MK2-mediated induction of miR-34c following DNA damage prevents Myc-dependent DNA replication. Proc Natl Acad Sci U S A. 2010;107:5375–5380. doi: 10.1073/pnas.0910015107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Braun CJ, Zhang X, Savelyeva I, Wolff S, Moll UM, Schepeler T, et al. p53-Responsive micrornas 192 and 215 are capable of inducing cell cycle arrest. Cancer Res. 2008;68:10094–10104. doi: 10.1158/0008-5472.CAN-08-1569. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Georges SA, Biery MC, Kim SY, Schelter JM, Guo J, Chang AN, et al. Coordinated regulation of cell cycle transcripts by p53-Inducible microRNAs, miR-192 and miR-215. Cancer Res. 2008;68:10105–10112. doi: 10.1158/0008-5472.CAN-08-1846. [DOI] [PubMed] [Google Scholar]
  • 40.Suzuki HI, Yamagata K, Sugimoto K, Iwamoto T, Kato S, Miyazono K. Modulation of microRNA processing by p53. Nature. 2009;460:529–533. doi: 10.1038/nature08199. [DOI] [PubMed] [Google Scholar]
  • 41.Chaudhry MA. Real-time PCR analysis of micro-RNA expression in ionizing radiation-treated cells. Cancer Biother Radiopharm. 2009;24:49–56. doi: 10.1089/cbr.2008.0513. [DOI] [PubMed] [Google Scholar]
  • 42.Chaudhry MA, Sachdeva H, Omaruddin RA. Radiation-induced micro-RNA modulation in glioblastoma cells differing in DNA-repair pathways. DNA Cell Biol. 2010;29:553–561. doi: 10.1089/dna.2009.0978. [DOI] [PubMed] [Google Scholar]
  • 43.Kann HE, Jr, Kohn KW. Effects of deoxyribonucleic acid-reactive drugs on ribonucleic acid synthesis in leukemia L1210 cells. Mol Pharmacol. 1972;8:551–560. [PubMed] [Google Scholar]
  • 44.Desai SD, Zhang H, Rodriguez-Bauman A, Yang JM, Wu X, Gounder MK, et al. Transcription-dependent degradation of topoisomerase I-DNA covalent complexes. Mol Cell Biol. 2003;23:2341–2350. doi: 10.1128/MCB.23.7.2341-2350.2003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Khobta A, Ferri F, Lotito L, Montecucco A, Rossi R, Capranico G. Early effects of topoisomerase I inhibition on RNA polymerase II along transcribed genes in human cells. J Mol Biol. 2006;357:127–138. doi: 10.1016/j.jmb.2005.12.069. [DOI] [PubMed] [Google Scholar]
  • 46.Sordet O, Larochelle S, Nicolas E, Stevens EV, Zhang C, Shokat KM, et al. Hyperphosphorylation of RNA polymerase II in response to topoisomerase I cleavage complexes and its association with transcription- and BRCA1-dependent degradation of topoisomerase I. J Mol Biol. 2008;381:540–549. doi: 10.1016/j.jmb.2008.06.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Solier S, Logette E, Desoche L, Solary E, Corcos L. Nonsense-mediated mRNA decay among human caspases: the caspase-2S putative protein is encoded by an extremely short-lived mRNA. Cell Death Differ. 2005;12:687–689. doi: 10.1038/sj.cdd.4401594. [DOI] [PubMed] [Google Scholar]
  • 48.Zhou Y, Gwadry FG, Reinhold WC, Miller LD, Smith LH, Scherf U, et al. Transcriptional regulation of mitotic genes by camptothecin-induced DNA damage: microarray analysis of dose- and time-dependent effects. Cancer Res. 2002;62:1688–1695. [PubMed] [Google Scholar]
  • 49.Lotito L, Russo A, Bueno S, Chillemi G, Fogli MV, Capranico G. A specific transcriptional response of yeast cells to camptothecin dependent on the Swi4 and Mbp1 factors. Eur J Pharmacol. 2009;603:29–36. doi: 10.1016/j.ejphar.2008.12.002. [DOI] [PubMed] [Google Scholar]
  • 50.Zhang HF, Tomida A, Koshimizu R, Ogiso Y, Lei S, Tsuruo T. Cullin 3 promotes proteasomal degradation of the topoisomerase I-DNA covalent complex. Cancer Res. 2004;64:1114–1121. doi: 10.1158/0008-5472.can-03-2858. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS494533-supplement-1.pdf (2MB, pdf)

RESOURCES