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. 2025 Mar 13;300(1):31. doi: 10.1007/s00438-025-02226-w

Proto-oncogene DEK binds to pre-mRNAs and regulates the alternative splicing of Hippo signaling genes in HeLa cells

Dongbo Liu 1, Wei Sun 1, Jing Han 1, Cong Wang 2, Dong Chen 2, Yunfei Wu 3, Yongjie Chang 3, Bin Yang 4,✉
PMCID: PMC11903584  PMID: 40075046

Abstract

Our study aimed to explore how DEK, a carcinogenic protein with chromatin architectural function, genome-widely binds to RNA and affects the alternative splicing in cancer cells to decipher its molecular functions. To achieve this goal, cell phenotype experiments, RNA sequencing (RNA-seq), and improved RNA immunoprecipitation sequencing (iRIP-seq) were conducted to identify the function and regulated targets of DEK in HeLa cells. The results showed DEK overexpression promoted cell proliferation and invasion of HeLa cells. Meanwhile, DEK hardly affected transcript level expression of those high expressed genes, but splicing pattern of 411 genes was regulated by DEK in HeLa cells, which were enriched in Hippo signaling pathway. Moreover, DEK broadly bind the RNA of a total of 11, 112 genes, with a biased binding the 5’ splice site (5’SS) consensus GGUAA motifs at the CDS and intronic regions. In addition, 297 DEK-binding genes showed different splicing pattern after DEK overexpression in HeLa cells. These genes were enriched in Hippo signaling pathway including CSNK1D. The RT-qPCR and RIP-PCR confirmed that DEK can bind to CSNK1D to regulate its alternative splicing in HeLa cells. In summary, our results indicated DEK could broadly bind and regulate the pre-mRNA splicing process, which provide new insights of mechanisms that DEK functions in various biological processes including cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00438-025-02226-w.

Keywords: DEK, RNA binding proteins, Alternative splicing, Hippo signaling pathway

Introduction

DEK is first reported as a part of DEK-CAN fusion protein in patients with acute myelogenous leukemia (AML) (von Lindern, Fornerod et al. 1992), and then identified as a putative proto-oncogene that encodes a 375-amino acid protein with an estimated molecular mass of 43 kDa (Sierakowska, Williams et al. 1993). A lot of studies reported DEK has important functions in many biology processes and diseases including cell differentiation (Wise-Draper, Morreale et al. 2009; Koleva, Ficarro et al. 2012), hematopoiesis (Capitano and Broxmeyer 2017; Serrano-Lopez, Nattamai et al. 2018), arthritis (Sierakowska, Williams et al. 1993; Mor-Vaknin, Saha et al. 2017), schizophrenia (O’Donovan, Franco-Villanueva et al. 2018) and cancer (Privette Vinnedge, Kappes et al. 2013; Teng, Lang et al. 2018). In particular, DEK functions by overexpression in many types of cancers, including AML (Larramendy, Niini et al. 2002), uterine cervical cancer (Wu, Li et al. 2008), serous ovarian tumors (Han, Xuan et al. 2009), breast cancer (Liu, Wang et al. 2012), colorectal cancer (Lin, Piao et al. 2013; Lin, Piao et al. 2014), gastric adenocarcinoma (Piao, Shang et al. 2014), lung cancer (Wang, Lin et al. 2014), hepatocellular carcinoma (Yi, Liu et al. 2015), and esophageal squamous cell carcinoma (Matrka, Cimperman et al. 2018). DEK affect tumorigenesis by regulating proliferation, apoptosis, invasion, chemoresistance and epithelial to mesenchymal transition (EMT) (Ageberg, Gullberg et al. 2006; Khodadoust, Verhaegen et al. 2009; Privette Vinnedge, Benight et al. 2015; Feng, Liu et al. 2017; Yang, Gao et al. 2017; Zhou, Deng et al. 2018). However, the underlying regulatory mechanisms of DEK in cancer is not clear. Thus, it is important to explore the molecular mechanism by which DEK functions in cancer progression.

DEK is broadly reported as DNA binding protein with the SAP domain (Waldmann, Baack et al. 2003; Waldmann, Scholten et al. 2004; Sanden, Jarvstrat et al. 2014). Mechanistically, DEK functions by taking part in many nuclear process including heterochromatin stability (Kappes, Waldmann et al. 2011), DNA replication (Alexiadis, Waldmann et al. 2000; Deutzmann, Ganz et al. 2015), DNA damage repair (Kavanaugh, Wise-Draper et al. 2011), epigenetic modification (Sawatsubashi, Murata et al. 2010) and transcriptional regulation (Heinz, Benner et al. 2010; Sanden, Jarvstrat et al. 2014). The DEK protein changes chromatin topology by introducing positive supercoils and assembles DNA and histones into chromatin (Waldmann, Eckerich et al. 2002; Sawatsubashi, Murata et al. 2010). DEK counteracts replication stress and ensures proliferative advantage by resolving problematic DNA and/or chromatin structures at the replication fork (Deutzmann, Ganz et al. 2015). DEK knockdown in vitro sensitizes cancer cells to DNA damaging agents and induces cell death via p53-dependent and -independent mechanisms (Kavanaugh, Wise-Draper et al. 2011). DEK could be recruited to the appropriate promoter to induce the histone H3 and H4 hypoacetylation of chromatin (Ko, Lee et al. 2006). DEK mainly binds to highly expressed genes but can act to either promote or repress their transcription (Sanden, Jarvstrat et al. 2014). Thus, DEK could regulate the gene expression via its DNA binding activity.

Actually, DEK and other several proteins were stably deposited on mRNAs by spliceosome (Le Hir, Izaurralde et al. 2000). Further study showed that DEK associates with splicing complexes through interactions mediated by SR-related proteins and remains bound to the exon-product RNA after splicing (McGarvey, Rosonina et al. 2000), which is responsible for enhancing nucleocytoplasmic export of spliced mRNAs (Le Hir, Gatfield et al. 2001). It has been reported that DEK with phosphorylation at 19 and 32 serines, enforces U2AF 3’ splice site discrimination, which is required for intron removal (Soares, Zanier et al. 2006). Moreover, a study show that endogenous phosphorylated DEK but not unphosphorylated DEK significantly enhanced the tropomyosin RNA splicing efficiency (Babaei-Jadidi, Li et al. 2011). In addition, DEK overexpression could influence exon expression and mis-spliced mRNAs of genes with the unmodified whole transcriptional level in AML cells (Mohamed, Balsat et al. 2016), indicating that DEK has multiple functions of RNA-binding proteins (RBPs). RBPs play vital roles in coordinating RNA processing events including pre-mRNA splicing and polyadenylation, mRNA transport and translation, creating enormous opportunities to mediate post-transcriptional gene regulation (Castello, Fischer et al. 2013; Fu and Ares 2014; Gerstberger, Hafner et al. 2014). Therefore, DEK should play vital roles in post-transcriptional regulation via its RNA binding activity. However, it still lacks evidence about the genome-widely binding characteristic of DEK on pre-mRNA and its regulation of alternative splicing.

To explore the genome-widely binding profile on pre-mRNA and regulation of alternative splicing of DEK, which might affect cancer progress, we obtained DEK-regulated transcriptomes in HeLa cells by improved RNA immunoprecipitation sequencing (iRIP-seq) and transcriptome sequencing (RNA-seq). Comparative transcriptome analysis revealed that overexpression of DEK led to an extensive change of expression and alternative splicing profiles in HeLa cells. We then analyzed targets bound by DEK in HeLa cells, showing that DEK broadly binds to transcripts with a bias at 5’SS consensus GGUAA motif and at the CDS and intron region. These results indicated that DEK could directly bind pre-mRNAs to regulate their alternative splicing.

Materials and methods

Cell culture and plasmid overexpression

HeLa cells (CL-0101, Procell Life Science & Technology Co., Ltd., China) were cultured at 37 °C, 5% CO2 in DMEM containing 10% fetal bovine serum, penicillin (100 U/µl) and streptomycin (100 g/mL). HeLa cells were transfected with vector containing DEK (DEK overexpression) or empty vector (control) by Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. After 48 h, RT-qPCR and Western blotting were performed to detect mRNA and protein expression levels of DEK in HeLa cells, respectively.

Cell proliferation and invasion experiments

The cell proliferation assay was performed using the cell counting kit-8 (CCK-8,40203 ES 76, Yeasen, Shanghai, China) and following the method in published paper (Paizula, Wulaying et al. 2024). Four time points (0, 24, 48 and 72 h) were chosen to assess the proliferation rate, which was calculated as the ratio between DEK-OE OD and control OD. Cell proliferation assay was also performed using the BeyoClick™ EdU-488 cell proliferation assay kit. For the DEK-OE and Ctrl cells, appropriate cell volume per well were seeded in 96 well plates. Cells from the DEK-OE and Ctrl groups were treated accordingly and cultured at 37℃ and 5% CO2 for 48 h before performing EdU experiments.

In vitro invasion assays were performed using transwell chambers (3422, Corning, USA) and following the method in published paper (Paizula, Wulaying et al. 2024). Briefly, 105 cells were added to the inserts and incubated for 2 h. The invasion cells were observed and counted under inverted microscope (MF52-N, Mshot, China) at 200× magnification.

RNA extraction and sequencing

Total RNA of HeLa cells was extracted according to the hot phenol method. Two phenol-chloroform treatment were performed to purify the RNA. Then the RNA was treated with RQ1 DNase (Promega, Madison, WI, USA) to eliminate DNA. Smartspec Plus (BioRad, USA) were used to measure the quality and quantity of the purified RNA by detecting the absorbance at 260 nm/280 nm (A260/A280). Agarose gel electrophoresis (1.5%) was performed to determine the RNA integrity. For RNA-seq library preparation of each sample, 10 µg total RNA were used for purification concentration of polyadenylated mRNAs with oligo(dT)-conjugated magnetic beads (Invitrogen, Carlsbad, CA, USA). Then, the purified mRNAs were fragmented at 95°C, with end repair and 5’ adaptor ligation. RT primers harbouring a 3’ adaptor sequence and a randomized hexamer were used to reverse transcription. The obtained cDNAs were purified and amplified. The cDNAs were stored at -80 °C until sequencing. For high-throughput sequencing, the libraries were prepared with ScriptSeq RNA-seq Library Preparation Kit (SSV21124, Illumina) following the manufacturer’s instructions and applied to Illunima HiSeq X Ten system for 150 nt paired-end sequencing (ABlife Inc., Wuhan, China).

RNA-seq raw data preprocessing and quality control

Each group was repeated with two biological replicate RNA-seq samples. Initially, raw reads with more than 3-N bases were discarded, then FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/) was used to remove adapters and low-quality bases with quality score below 20, and short reads of less than 16nt were also discarded to obtain clean reads. Next, the clean reads were mapped to the human GRCh38 genome by TopHat2 (Kim, Pertea et al. 2013) allowing 4 mismatches, and only uniquely mapped reads were used to perform subsequent analyses to ensure the reliability of the results. According to RNASeqPower, this experimental design has a statistical power of 0.89.

Differentially expressed genes (DEGs) and functional analysis

The expression levels of genes were determined by FPKM (paired-end fragments per kilobase of exon per million fragments mapped) using an in-house pipeline. The edgeR software (Robinson et al. 2010) were used to identify the differentially expressed genes (DEGs). The fold change (fold change ≥ 2 or ≤ 0.5) and false discovery rate (FDR < 0.05) were used as the DEG threshold.

Gene Ontology (GO) analysis and enriched KEGG pathway analysis were used to annotate the function of DEGs by KOBAS 2.0 server (Xie, Mao et al. 2011). The enrichment of each GO terms or KEGG pathway were evaluated by the hypergeometric test and Benjamini-Hochberg FDR controlling procedure (corrected p-value < 0.05).

Alternative splicing analysis

The ABLas pipeline were performed to identify the alternative splicing (AS) events (ASEs) and regulated alternative splicing events (RASEs) between DEK overexpression and control HeLa cells (Xia, Chen et al. 2017). In brief, splice junction reads were used to identify eight types of ASEs. Those ASE types were alternative 5’ splice site (A5SS), alternative 3’ splice site (A3SS), cassette exon, exon skipping (ES), mutually exclusive exon skipping (MXE), the MXE combined with an alternative 5’ promoter (5pMXE), an alternative polyadenylation site (3pMXE) and intron retention (IR). Alternatively spliced reads and constitutively spliced reads were used to calculate the RASE ratio. Then, Fisher’s exact test was used to compare the RASE ratio between DEK overexpression and control HeLa cell. The p-value < 0.05 and RASE ratio > 0.15 were used as the RASE threshold.

RT-qPCR validation of DEGs and RASEs

Reverse transcription and quantitative PCR (RT-qPCR) of some selected DEGs and RASEs was performed to validate the RNA-seq results. GAPDH was used as reference gene. RT-qPCR were conducted with the same RNA samples for RNA-seq. The PCR reaction conditions were as followed: denaturing 95 ˚C for 10 min, 40 cycles of denaturing at 95 ˚C for 15 s, and annealing and extension at 60 ˚C for 1 min. Each sample had three technical replicates. The primers for RT-qPCR analysis were listed in Table S1.

Co-immunoprecipitation

DEK-overexpressed HeLa cells were first lysed in ice-cold lysis buffer for 5 min. The lysis buffer was prepared as followed: 10 mM HEPES, pH 7.0, 100 mM KCl, 5mM MgCl2, 0.5% NP-40, 10 mM DTT, 200 U/ml RNase inhibitor (Promega) and a protease inhibitor (Roche). Then, cell debris of the samples were removed by centrifugation (13,000 × g for 20 min). Then, the supernatant was added with 100 µl DynaBeads protein G (Life Technologies) to pre-cleared at 4℃ for 30 min. The pre-cleared supernatant was incubated with DynaBeads protein G conjugated with anti-flag antibody (Sigma, F7425) or normal IgG at 4℃ for 6 h. The beads were washed with lysis buffer for six times. The washed beads were divided into two groups, which were used for RNA isolation and Western blotting assay.

Western blotting analysis

The washed beads were resuspended with 40ul Elution Buffer (50mM Tris-Cl with PH 8.0, 10mM EDTA with PH 8.0) and 1% SDS, which were incubated at 70 °C for 20 min. Then, the samples were centrifuged at 13,200 × g for 30 s and were put on the magnetic separator. The supernatant was transferred to a new eppendorf (EP) tube. Then supernatant was eluted by boiling for 10 min with 1 × SDS sample buffer. The supernatants were analyzed on a 10% SDS-PAGE gel. Then, the supernatants were transferred onto a PVDF membrane (Millipore). Membranes were incubated with primary antibody: monoclonal Flag antibody (1:2,000, Sigma), GAPDH (1:1,000, CUSABIO) and then with an HRP-conjugated secondary antibody. Bound secondary antibody (anti-mouse or anti-rabbit 1:10,000) (Abcam) was detected using the enhanced chemiluminescence (ECL) reagent (Thermo).

The iRIP-seq library preparation and sequencing

TRIzol (Invitrogen) were used to isolate the DEK-RNAs complexes from the washed beads. The Balancer NGS Library Preparation Kit for small/microRNA (Gnomegen) were to prepare complementary DNA (cDNA) libraries. The libraries were prepared following the manufacturer’s instructions and applied to the Illumina Nextseq 500 system for 151 nt paired-end sequencing by ABlife. Inc (Wuhan, China).

Peak calling analysis of iRIP-seq

Clean reads after quality filtering were aligned onto the genome and only uniquely mapped reads were used for the following analysis. DEK binding regions were identified by ABLIRC strategy as previous described (Xia, Chen et al. 2017). In brief, uniquely mapped reads with at least 1 bp overlap were clustered to identify bind peaks. Random reads with the same number and length as reads in peaks for each gene were generated by computational simulation, which were further mapped to the same gene to generate random max peak height. The computational simulation process was repeated for 500 times. All the observed peaks that their heights were higher than those of random max peaks (p-value < 0.05) were used for further analysis. The computational simulation was independently performed for the DEK-IP and Input control samples. Moreover, the peaks resulted from DEK-IP samples were removed if it had overlap with peaks resulted from Input control samples peaks. The gene with filtered peaks from DEK-IP samples were the finally DEK binding target. Homer software were performed to identify the binding motif of DEK (Heinz, Benner et al. 2010).

Statistical analysis and other methods

The RT-qPCR data were presented as the mean ± standard deviation (SD). Difference of RT-qPCR data between two groups were compared with an unpaired two-tailed Student’s t-test with P values < 0.05 being statistically significant. The expression analysis in TCGA database and survival analysis of DEK were calculated by GEPIA2 (Tang, Kang et al. 2019).

Results

DEK expression was dysregulated in cancers and associated with onco-phenotypes of HeLa cells

We first explored the expression levels of DEK in the cancer genome atlas (TCGA) database, and presented the expression pattern of DEK of various cancer types (Fig. 1A). Three cancers, including CHOL, ESCA, and KICH, showed significant difference between tumor and normal samples (Fig. 1A). Meanwhile, we found DEK expression levels were significantly associated with the survival time of patients in multiple cancer types (Table S2). However, higher expression levels of DEK were associated with better or worse prognosis results in different cancers (Table S2, Fig. 1B), suggesting that DEK may play distinct roles in different cancer types. Thus, it is important to decipher the underlying regulatory mechanisms of DEK in tumor cells. We chose HeLa cell as the research object in this study. To further explore the transcriptional role of DEK, we overexpressed DEK in HeLa cell line. HeLa cells were transfected by vector with (DEK-OE) or without (control) DEK transcript sequences. Both the RT-qPCR and Western blotting results showed that DEK was significantly overexpressed after transfecting with the DEK expression vector in HeLa cells (Fig. 1C-D, Fig. S1). Cellular proliferation assessment by CCK8 and Edu experiments demonstrated that DEK-OE significantly increased the proliferation level of HeLa cells (Fig. 1E-F). The cellular invasion assay also demonstrated that DEK-OE enhanced the invasion ability of HeLa cells (Fig. 1G). These results indicate that DEK has pro-carcinogenic function in HeLa cells.

Fig. 1.

Fig. 1

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DEK expression was dysregulated in cancers and associated with onco-phenotypes of HeLa cells. (A) The expression levels of DEK in multiple cancer types from TCGA. Red star indicated significant difference between normal and tumor. (B) Survival time curve of patients by dividing them into two groups according to the expression levels of DEK in ACC, KICH, and KIRC. (C) DEK overexpression were validated by qRT-PCR. (D) DEK overexpression were validated by Western blotting. (E) DEK overexpression significantly increased proliferation level of HeLa cells by CCK8. (F) The increased proliferation level by DEK overexpression using Edu method. (G) DEK overexpression significantly increased invasion ability of HeLa cells. * P < 0.05, *** P < 0.001, **** P < 0.0001

DEK overexpression showed no significant effects on transcript level of most high expressed genes

Then, the gene expression profiles of DEK-OE and control cells were detected by RNA-seq. For DEK-OE and control HeLa cells, RNA-seq libraries were prepared and sequenced with two biological replicates for each group (DEK_1st, DEK_2nd, Ctrl_1st, Ctrl_2nd). An average of about 85 million clean pair-end reads per sample were obtained after removing sequence adaptors and low-quality reads. After mapping the clean reads to the human genome, an average of about 71 million uniquely mapped read pairs per sample were obtained (Table S3).

Then, FPKM values representing gene expression levels were calculated based on those uniquely mapped reads. There were 25,167 expressed genes (FPKM > 0) and 12,529 genes expressed at an expression level of FPKM > 1 in at least one sample (Table S4 and Table S5). Overexpression of DEK was further validated by FPKM values of DEK in HeLa cells (Fig. 2A). Then, we identified the DEGs between DEK-OE and control cells to reveal the effect of DEK overexpression on gene expression profile of HeLa cells. A total of 489 up-regulated and 504 down-regulated genes after DEK overexpression (Fig. 2B). FPKMs and fold changes of these DEGs were presented in the Table S6. Then, DEK-OE and control samples could be separated according to the hierarchical clustering of normalized FPKM values of DEGs (Fig. 2C). However, the percentage of DEGs with FPKM > 1 at one sample were about 20% (Fig. 2D). These results indicated that DEK-OE possibly had no obvious effect on transcript levels of highly expressed genes, although it is likely to be limited by the low sample numbers resulting in insufficient statistical power for detection. Finally, RT-qPCR was performed to validate the changes in mRNA levels of DEGs after DEK-OE in HeLa cell. A total of six DEGs including IFI27L2, DET1, GKAP1, LMO1, PRSS27 and CYB5D2, were randomly selected for RT-qPCR validation. As shown, expression levels of all the selected DEGs were significantly changed in DEK-OE samples, which coincided with RNA-seq results (Fig. 2E).

Fig. 2.

Fig. 2

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Effects of DEK overexpression on the gene expression profile of HeLa cell. A. DEK overexpression was validated by RNA-seq. B. DEGs were identified after DEK overexpression. Red and blue dot represent upregulated and downregulated genes in the volcano plot, respectively. C. Heatmap and hierarchical cluster of DEGs between control and DEK overexpression samples. FPKM values are log2-transformed and then median-centred by each gene. D. Percentage of high and low expressed genes among DEGs. FPKM > 1 at least one sample indicated high expressed gene. FPKM < 1 at all sample indicated low expressed genes. E. Relative expression levels of DEGs identified by RNA-seq (left) were validated by RT-qPCR (right). For RT-qPCR, GAPDH was used as the reference gene. Student’s t test was performed to compare DEK-OE and control cells with significance set at a P value of less than 0.05. * P < 0.05, ** P < 0.01

DEK regulates the alternative splicing of genes enriched in gene expression, transcription regulation and mitotic cell cycle

As reported, phosphorylated DEK interacts with spliceosome to play an important role in regulating alternative splicing (McGarvey, Rosonina et al. 2000; Soares, Zanier et al. 2006). Thus, we explored whether DEK overexpression genome-widely affects alternative splicing in HeLa cells. After mapping the splice reads from DEK-OE and control HeLa cells (Table S3) to the reference genome, we detected a total of 237,367 annotated exons (64.6% of total annotated ones). Then, TopHat2 were used to identify junction site, which resulted a total of 157,489 known and 138,163 novel splice junctions. The ABLas pipeline were used to identify AS events from the splice junctions (Xia, Chen et al. 2017), which resulted in 18,709 known ASEs and 56,474 novel ASEs (Table S7).

Then, a custom pipeline was used to identify the high-confidence consensus RASEs by DEK. The AS ratio changes between DEK-OE and control cells were compared at a cutoff of p-value ≤ 0.05 and changes in the AS ratio ≥ 0.15, which resulted in a total of 440 RASEs, including 152 intron-retention (IR) and 288 non-IR (NIR) RASEs (Table S8). It was notable that the number of RASEs including A3SS, A5SS, ES and cassette exons were relatively high (Fig. 3A). These RASEs occurred in a total of 411 genes, which was defined as DEK-regulated alternative splicing genes (RASGs). However, DEGs and RASGs didn’t overlap (Fig. 3B). These results indicated that DEK-OE did not affect the expression of DEGs by regulating their alternative splicing in HeLa cells. Subsequently, GO analysis was conducted to reveal the potential function of RASGs. As shown, RASGs were enriched in GO molecular functional terms including protein binding, DNA binding, ATP binding, peptidase activity and metal ion binding (Fig. 3C; Table S9). Moreover, RASGs were also enriched in GO biological process terms, including mitotic cell cycle, transcription of DNA-dependent, gene expression, DNA repair (Fig. 3D; Table S9). These results indicated that DEK functions by regulating the alternative splicing of genes with different roles.

Fig. 3.

Fig. 3

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Analysis of alternative splicing pattern after DEK overexpression in HeLa cell. (A) Frequency distribution of different types of alternative splicing events regulated by DEK. (B) The overlap analysis between differentially expressed genes (DEGs) and DEK-regulated alternative splicing genes (RASGs). (C) The top 10 GO molecular function analyses of RASGs. (D) The top 10 GO biological process analyses of RASGs

To confirm the effects of DEK overexpression on alternative splicing, the ratios of RASEs in DEK-OE and control cells were quantified by RT-qPCR. As shown, changes in the ratio of detected RASEs in RT-qPCR were consistent with the results of the RNA-seq (Fig. 4; Fig. S2). These RASEs occurred in YAP1, CCND3, SRSF6 and NCOR1. GO analysis showed these genes had protein binding and DNA binding activities, which might affect the mitotic cell cycle, gene expression and DNA repair and mitosis (Table S9). In particular, SRSF6 was spliced at an exon after DEK overexpression, which probably affects its RNA binding activity and regulation on splicing (Fig. 4A; Table S9). Exon inclusion of NCOR1 could affect the apoptotic process by DNA-dependent transcription (Fig. 4B; Table S9).

Fig. 4.

Fig. 4

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Validation of DEK-regulated alternative splicing events. (A) Exon skipping of SRSF6. (B) Exon skipping of NCOR1. IGV-sashimi plots show AS changes in DEK overexpression cells and control cells (left panel), and the transcripts for the gene are shown below. The schematic diagrams depict the structures of ASEs, AS1 (purple line) and AS2 (green line). The exon sequences are denoted by boxes and intron sequences by the horizontal line (right panel, top). RNA-seq quantification and qRT-PCR validation of ASEs are shown in the bottom right panel. The altered ratio of AS events in RNA-seq was calculated using the formula: AS1 junction reads / (AS1 junction reads + AS2 junction reads), while the altered ratio of AS events in qRT-PCR was calculated using the formula: AS1 transcript level / AS2 transcript level. Student’s t-test was performed to compare DEK-OE and control cells with significance set at a P value of less than 0.05. * P < 0.05, ** P < 0.01

DEK binds genes with functions annotation in gene expression, mitotic cell cycle and RNA splicing

To further explore whether DEK affect the alternative splicing by binding RNA, we employed iRIP-seq using Flag-tagged DEK in the HeLa cells. DEK protein was detected by western blotting in the IgG sample and IP sample, while no DEK was detected in the negative input control (Fig. 5A). The cDNA libraries from anti-Flag immunoprecipitates and the input control sample were sequenced, which resulted in an average 20 million clean reads for each sample (Table S10). After mapping clean reads on the whole human genome, we found that the DEK binding reads were highly enriched in 3’UTR, CDS regions and intronic regions, while only few reads were found in the intergenic region, which usually indicates the presence of active transcriptional activity in the DEK-binding region, suggesting that DEK binds RNA rather than DNA (Fig. 5B, Table S11). When compared with the enrichment in input controls, DEK binding reads enrich downstream of TSS and upstream of TTS (Fig. 5C). In particular, DEK binding reads showed a significant enrichment at the stat codon and stop codon in HeLa cells (Fig. 5C). The relatively not too strong correlation results suggested that the distribution of reads on genes in the IP samples differed from that in the input samples, implying a higher degree of enrichment and specificity of DEK-bound RNA (Fig. 5D). These results indicated that DEK could strongly bind to RNAs.

Fig. 5.

Fig. 5

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Transcriptome-wide landscape of DEK binding. (A) Immunoprecipitation efficiency of DEK were validated by Western blotting. (B) Distribution of DEK-binding reads on different genomic region. Fisher’s exact test was performed to compare DEK-IP and Input control with significance set at a P value of less than 0.05. * P < 0.05, ** P < 0.01. (C) Distribution DEK binding reads around transcription start/terminate sites (left) and translation start and stop codons (right). (D) Correlations between the IP and input samples were compared. A higher correlation coefficient (R) indicates a similar distribution of most reads on the chromosome in the two samples, suggesting a lower enrichment and specificity of the bound RNA, and vice versa

We further identified DEK-binding peaks from iRIP-seq reads, using ABLIRC pipeline (Xia, Chen et al. 2017). After filtering peaks shared by both DEK and input control samples, the ABLIRC algorithm identified 61,706 peaks distributed in 12,061 genes and 54,928 peaks distributed in 12,127 genes in two replicates, respectively. There were 17,778 overlapped peaks for the DEK-bound peaks between two replicates (Fig. 6A), which located in 9647 DEK-bound genes. In order to validate our results, we then predicted DEK-bound peaks using Piranha, a published software tool for the identification of RNA protein interaction sites from high-throughput sequencing data. A total of 8190 peaks called by ABLIRC were validated by Piranha for the first replicate (Fig. 6B, p-value = 1.023955e-233, hypergeometric test). We determined DEK-bound genes in HeLa cells by those containing the overlapped DEK-bound peak of both replicates recovered by ABLIRC. GO analysis showed DEK-bound genes were enriched in terms including “gene expression”, “mitosis”, “mitotic cell cycle”, “nuclear mRNA splicing via spliceosome” and “RNA splicing” (Fig. 6C; Table S12).

Fig. 6.

Fig. 6

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Characteristic analysis of DEK binding peak and validation of DEK binding Targets. (A) Venn diagram for overlapped DEK binding peaks between two replicates. (B) Venn diagram for overlapped DEK binding peaks identified by ABLIRC and Piranha. (C) The top 10 GO biology process analysis of DEK peak associated genes. (D) The top DEK binding motif based on the peak called by ABLIRC and Piranha. (E) DEK binding on selected target genes were validated by RIP-PCR. (F) The reads density landscape of DEK-bound peaks on GKAP1 and PRSS27, which were validated by RIP-PCR. Student’s t test was performed to compare DEK-OE and control cells with significance set at a P value of less than 0.05. Rep1 and Rep2 respectively indicate two different groups of IP samples. * P < 0.05, ** P < 0.01

Furthermore, HOMER was employed to discover the sequence motifs enriched within DEK binding peak. The GUAAG motif was the top abundant one in both replicates for the binding peaks identified by ABLIRC and Piranha, respectively (Fig. 6D; Fig. S3). These results indicated DEK bind RNA at specifically sequence. RIP-PCR were used to validate some DEK bound genes including TEAD2, YAP1, CCND3, DEK, GKAP1 and PRSS27. The results showed that all candidate RNA targets had significant enrichment in the anti-DEK immunoprecipitate relative to the input control (Fig. 6E). For example, we showed the reads density landscape of DEK-bound peaks on the GKAP1 and PRSS27 transcripts (Fig. 6F). In summary, these results demonstrate that DEK extensively binds to transcripts and probably affects their process and function.

DEK binds to Hippo signaling genes to affect their alternative splicing

To determine whether DEK regulates the alternative splicing by binding target genes. We found that there was a significant overlap between RASGs and DEK-bound genes (Fig. 7A, p-value = 2.132218e-49, hypergeometric test). GO analysis showed these overlapped genes were enriched terms including mitotic cell cycle, gene expression, and DNA repair (Fig. 7B). KEGG analysis showed these genes were enriched in pathway including regulation of autophagy, longevity regulating pathway and Hippo signaling pathway (Fig. 7C). Importantly, the Hippo pathway is considered to be an important signaling pathway regulating cancer progression, and some components of this pathway could affect the proliferation of cancer cells when they are dysregulated (Mohammadi, Arefnezhad et al. 2020). Then, we focused on the genes from the Hippo signaling pathway, including CSNK1D, YAP1, CCND3. The alternative splicing of YAP1 and CCND3 was significantly changed after DEK overexpression in HeLa cell (Fig. S2).

Fig. 7.

Fig. 7

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DEK binds to targets and regulates their alternative splicing. A. The overlap analysis of DEK regulated alternative splicing genes and DEK binding genes. B. The top 10 GO biology process analysis of DEK bound RASGs. C. The top 10 KEGG pathway analyses of DEK bound RASGs. D. Statistics analysis of DEK-bound peaks overlapped with the 5’ and 3’ splice sites (5SS and 3SS). E. The reads density landscape of DEK-bound peaks that overlapped with splice site of an exon skipping events on CSNK1D. F. DEK binding on CSNK1D were validated by RIP-PCR, Rep1 and Rep2 indicate two biological replicates. G. Exon skipping event of CSNK1D were validated by qRT-PCR. The exon sequences are denoted by boxes and intron sequences by the horizontal line (right panel, top). Student’s t test was performed to compare DEK-OE and control cells with significance set at a P value of less than 0.05. * P < 0.05, ** P < 0.01

The DEK iRIP-seq data now offer an unbiased view on the actual location and binding motif of DEK on regulated genes. As enrichment of DEK bind peak in intron regions and CDS regions, we analyze distribution of splice site among the bind peak. The results showed that more than 4, 000 peaks were overlapped with the 5′ and 3′splice sites, respectively (Fig. 7D). These results indicated DEK might directly bind to these targets and regulate alternative splicing in HeLa cells. We further selected CSNK1D as an example to study the effect of DEK binding on the pre-mRNA splicing. Dysregulation of the expression and activity of CSNK1D has been observed in different types of cancers, and different transcription variants exhibit different functions (Xu, Ianes et al. 2019). As shown, a large number of DEK-bound peaks were found across the exonic locations of CSNK1D (Fig. 7E). Both RIP-seq and RIP-PCR experiment implied that CSNK1D is a direct target of DEK protein in HeLa cells (Fig. 7F). In addition, RT-qPCR showed pre-mRNA splicing pattern of CSNK1D changed after DEK was overexpressed in HeLa cell (Fig. 7G). These evidences are convincing enough that DEK binds and regulates the alternative splicing of many pre-mRNAs in Hippo signaling pathway to function in HeLa cells.

Discussion

As a DNA binding protein, DEK is involved in the regulation of transcription (Heinz, Benner et al. 2010; Sanden, Jarvstrat et al. 2014) and DEK overexpression contributes to the cancer processes (Grasemann, Gratias et al. 2005; Liu, Wang et al. 2012; Privette Vinnedge, Benight et al. 2015). In addition, several studies demonstrated that DEK participate in regulation of alternative splicing by interacting with spliceosome (Le Hir, Izaurralde et al. 2000; McGarvey, Rosonina et al. 2000; Le Hir, Gatfield et al. 2001; Soares, Zanier et al. 2006; Babaei-Jadidi, Li et al. 2011; Mohamed, Balsat et al. 2016). However, it still lacks evidence that DEK broadly interacts with RNAs to regulate their alternative splicing. In this study, we combined RNA-seq and iRIP-seq technologies to reveal DEK regulation of alternative splicing by globally bind to RNAs in HeLa cells. Our results showed that DEK overexpression affects the alternative splicing of many genes and DEK broadly binds to RNAs with a preference at the CDS and intron regions. In particular, DEK showed a specific binding feature with RNA 5’SS consensus GGUAA motif, which has not been reported before. Binding of 5’SS is prevalent, probably via its spliceosome interaction capability, which may powerfully affect alternative splice site choice and thusly modulate alternative splicing patterns. It follows that over 70% of RASGs (297 genes) showed DEK binding signal after considering the background noise of iRIP-seq, which demonstrates that DEK may be able to bind effectively to the target genes and affect their splicing patterns at a certain expression level. In addition, a variety of other transcription and splicing factors may be involved in the splicing process, and these factors may have antagonistic or synergistic effects on the action of DEK, resulting in only a small fraction of genes showing altered splicing patterns. In short, these results indicated that DEK was likely to directly bind RNA to regulate alternative splicing, which provide new insights of mechanisms by DEK functions in various biological processes.

DEK was broadly reported to be overexpressed in many forms of human cancers (Privette Vinnedge, Benight et al. 2015; Matrka, Cimperman et al. 2018; Teng, Lang et al. 2018). Different lines of previous studies have demonstrated DEK overexpression inhibit or promote the expression of single target genes (Adams, Bolanos et al. 2015; Zhang, Liu et al. 2016; Xu, Zou et al. 2017; Yang, Gao et al. 2017). Some studies also explore the effect of DEK on gene expression at genome-wide scale by expression array or RNA-seq after DEK knockdown in different cell lines, which all showed DEK regulate expression of thousands of genes at transcript level (Sanden, Jarvstrat et al. 2014; Adams, Bolanos et al. 2015; Lin et al. 2015; Mohamed, Balsat et al. 2016; Cifdaloz, Osterloh et al. 2017; Jia, Li et al. 2019). Here, we showed that DEK regulate the expression of 993 genes at transcript level by overexpressing DEK in HeLa cells. However, it is notable that a high percentage of these DEK-regulated genes showed low expression levels. Compared to primary human foreskin keratinocytes, DEK showed higher expression in HeLa cell, which inhibits apoptosis (Wise-Draper, Allen et al. 2006). In addition, DEK overexpression could influence exon expression and mis-spliced mRNAs of genes but did not modified whole transcriptional level in AML cells (Mohamed, Balsat et al. 2016). Therefore, further exogenous overexpression might not induce changes of gene expression at transcript level in HeLa cell.

Different lines of studies have shown that DEK plays important role in alternative splicing by interaction with spliceosome (Le Hir, Izaurralde et al. 2000; McGarvey, Rosonina et al. 2000). In particular, DEK with phosphorylated serines 19 and 32 enforces 3’ splice site discrimination of its interactor U2AF to remove intron (Soares, Zanier et al. 2006). Here, we showed DEK overexpression could globally change the alternative splicing events of HeLa cell by RNA-seq, which is consistent with effect of DEK-knockdown on mRNA splicing in AML cells (Mohamed, Balsat et al. 2016). In particular, genes with changed alternative splicing pattern were enriched in pathway including mitotic cell cycle, which could affect the proliferation of HeLa cells. However, a previous study suggested that endogenous phosphorylated DEK rather than unphosphorylation significantly affected the splicing of tropomyosin (Babaei-Jadidi, Li et al. 2011), and whether exogenous DEK overexpression affects phosphorylation level of DEK protein deserves further investigation.

In addition, these previous work indicated DEK could directly or indirectly interact with RNA (Le Hir, Izaurralde et al. 2000; McGarvey, Rosonina et al. 2000; Soares, Zanier et al. 2006). Two more recently studies revealed that DEK is an RBP (Kwon, Yi et al. 2013; Sebestyen, Singh et al. 2016). Here, we reported the genome-widely binding characteristic of DEK on pre-mRNA. Our results showed DEK could bind pre-mRNA/mRNA of 12,127 genes, and specifically bind GUAAG around the start codon and stop codon in HeLa cells. Importantly, alternative splicing pattern of a lot of DEK bound genes changed after DEK overexpression in HeLa cell. In particular, serval genes of Hippo signaling pathway changed the alternative splicing pattern after DEK overexpression. Dysfunction of the Hippo signaling pathway has been implicated in an increasing number of human diseases, including cancer (Zheng and Pan 2019). Moreover, we found a novel alternative splicing event on CSNK1D, which might result in new transcription variants (TV). In fact, different CSNK1D TVs exhibit different functions, and highly specific isoform- or even TV-specific therapeutics which could be of use in personalized therapy concepts for the treatment of neurodegenerative diseases and cancer (Xu, Ianes et al. 2019). Of course, more work should be conducted to validate whether DEK overexpression will induce novel CSNK1D isoforms and their functions in cervical cancer and other cancer types in future. Meanwhile, the long read or full-length mRNA sequencing is a powerful technology to elucidate the role of DEK in regulating mRNA splicing and isoform production, and can be used in future study.

Conclusions

In summary, DEK functions as an RBP in RNA processing that are distinct from its roles in transcriptional deregulation. In this study, we conclude that DEK broadly regulate the alternative splicing of genes enriched in cancer-associated pathways by broadly binding to RNA 5’SS consensus GUUAA motifs a 5’SS of CDS and intron regions. The dysregulated alternative splicing of cancer associated genes will result in functional loss or gain by truncated protein and nonsense-mediated mRNA decay, which could promote the cancer progress (McGlincy and Smith 2008). These results provide a basis for further study of post-transcriptional regulation mechanisms by DEK in various biological processes of cancer development.

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Acknowledgements

Not applicable.

Author contributions

BY and DL contributed to the study design. DL, WS, JH, CW, DC, YW, and YC performed the experiment and/or performed data analysis. DL, WS and BY prepared the manuscript. All authors read and approved the final manuscript.

Funding

No funding was received.

Data availability

All data generated or analyzed during this study have been included in this published article and its supplementary materials. The datasets supporting the results of this article are available in NCBI’s Gene Expression Omnibus through GEO series accession number GSE141567. To review GEO accession GSE141567: Go to https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE141567. Enter token kzcnwycmfnmbzkp into the box.

Declarations

Ethical approval

Not applicable.

Consent for publication

Not applicable.

Competing interests

Cong Wang, Dong Chen, Yunfei Wu, and Yongjie Chang are employed by Wuhan Ruixing Biotechnology Co. Ltd.

Footnotes

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Supplementary Materials

Supplementary Material 1 (1,018.9KB, docx)
Supplementary Material 2 (10.1KB, xlsx)
Supplementary Material 3 (11.2KB, xlsx)
Supplementary Material 4 (10.1KB, xlsx)
Supplementary Material 5 (1.9MB, xlsx)
Supplementary Material 6 (9.5KB, xlsx)
Supplementary Material 7 (213.2KB, xlsx)
Supplementary Material 8 (139.2KB, xlsx)
Supplementary Material 9 (10.4KB, xlsx)
Supplementary Material 10 (131.2KB, xlsx)
Supplementary Material 11 (20.9KB, xlsx)
Supplementary Material 12 (9.7KB, xlsx)
Supplementary Material 13 (10KB, xlsx)

Data Availability Statement

All data generated or analyzed during this study have been included in this published article and its supplementary materials. The datasets supporting the results of this article are available in NCBI’s Gene Expression Omnibus through GEO series accession number GSE141567. To review GEO accession GSE141567: Go to https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE141567. Enter token kzcnwycmfnmbzkp into the box.

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