Quantitative Proteomics Reveals the Regulatory Networks of Circular

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Quantitative Proteomics Reveals the Regulatory Networks of Circular RNA CDR1as in Hepatocellular Carcinoma Cells Yang Xue, Qian Xiong, Ying Wu, Siting Li, and Feng Ge J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00519 • Publication Date (Web): 11 Sep 2017 Downloaded from http://pubs.acs.org on September 12, 2017

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Journal of Proteome Research

Quantitative Proteomics Reveals the Regulatory Networks of Circular RNA CDR1as in Hepatocellular Carcinoma Cells

Xue Yang1,2#, Qian Xiong1#, Ying Wu1,2, Siting Li1,2, Feng Ge1 *

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Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences,

Wuhan 430072, China 2

University of Chinese Academy of Sciences, Beijing 100049, China

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These authors contributed equally to this work

*To whom correspondence should be addressed: Feng Ge, Institute of Hydrobiology, Chinese Academy of Sciences, E-mail: [email protected]. Phone/Fax: +86-27-68780500

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ACS Paragon Plus Environment


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Abstract Circular RNAs (circRNAs), a class of widespread endogenous RNAs, play crucial roles in diverse biological processes and are potential biomarkers in diverse human diseases and cancers. Cerebellar degeneration-related protein 1 antisense RNA (CDR1as), an oncogenic circRNA, is involved in human tumorigenesis and is dysregulated in hepatocellular carcinoma (HCC). However, the molecular mechanisms underlying CDR1as functions in HCC remain unclear. Here, we explored the functions of CDR1as and searched for CDR1as-regulated proteins in HCC cells. A quantitative proteomics strategy was employed to globally identify CDR1as-regulated proteins in HCC cells. In total, we identified 330 differentially expressed proteins (DEPs) upon enhanced CDR1as expression in HepG2 cells, indicating that they could be proteins regulated by CDR1as. Bioinformatic analysis revealed that many DEPs were involved in cell proliferation and the cell cycle. Further functional studies of epidermal growth factor receptor (EGFR) found that CDR1as exerts its effects on cell proliferation at least in part through the regulation of EGFR expression. We further confirmed that CDR1as could inhibit the expression of microRNA-7 (miR-7). EGFR is a validated target of miR-7; therefore, CDR1as may exert its function by regulating EGFR expression via targeting miR-7 in HCC cells. Taken together, we revealed novel functions and underlying mechanisms of CDR1as in HCC cells. This study serves as the first proteome-wide analysis of a circRNA-regulated protein in cells and provides a reliable and highly efficient method for globally identifying circRNA-regulated proteins.

Keywords: Circular RNAs; CDR1as; Hepatocellular Carcinoma; Quantitative proteomics; EGFR; miR-7

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Introduction Hepatocellular carcinoma (HCC) is the fifth most common cancer and the third leading cause of cancer-related death in the world.1 Although important advances have been made in diagnosis and therapy in recent years, the clinical outcome for HCC patients has remained unsatisfactory because of disease relapse, late diagnosis and drug resistance.2, 3 It is well accepted that HCC develops through a multistep transformation process with the accumulation of genetic and epigenetic changes.4,

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Therefore, understanding the exact

mechanisms underlying the initiation and metastasis of HCC, especially genetic and epigenetic alterations, will help in the identification of novel diagnostic biomarkers and the development of new therapeutic strategies. Circular RNAs (circRNAs) are a novel class of non-coding RNAs (ncRNAs) that are ubiquitous in all eukaryotic cells.6 CircRNAs are a special type of ncRNAs without 5’ caps and 3’ tails, resulting in covalently closed continuous loops.7, 8 Due to the low expression level and noncoding properties, circRNAs were considered as transcriptional noise with little functional potential since they were first described in 1976.9 In the last few years, with the development of RNA sequencing technology and improved bioinformatic software, more and more different circRNAs were identified in a variety of organism and their mechanism of formation and their biological functions were gradually revealed.10-16 Increasing evidence demonstrated that circRNAs play critical functions in a variety of cellular activities including cell growth,11, 17 cell invasion18 and cell cycle,19 and are involved in many human diseases and cancers.10, 18, 20 Additionally, circRNAs are stable and tissue-specific expression, making them very attractive candidates for diagnostic biomarkers and therapeutic targets.21-24 Among them, CDR1as (also known as ciRS-7) stands out as an oncogenic circRNAs25 and is involved in human tumorigenesis.26,

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It is dysregulated in various cancers, including hepatocellular

carcinoma (HCC).28, 29 CDR1as is predominantly found in human brain and is ~1,500 nucleotides in length.25 One recent study showed that CDR1as functions as an miR-7 sponge/inhibitor in the embryonic zebrafish.30, 31 This means that CDR1as binds to miR-7, consequently arresting 3



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miR-7 and therefore increasing the level of miR-7 targets, revealing a new mechanism for the regulation of miRNA function in cells.25 As a tumor inhibitor, miR-7 regulates a number of biological processes,30, 32, 33 and its deregulation can be associated with the signaling pathways of a variety of cancers, including HCC.29, 34, 35 It is conceivable that as a powerful miR-7 sponge/inhibitor, CDR1as will also affect these pathways. Therefore, CDR1as is a circRNA with important functions, and there is an increasing number of reports on the functions of CDR1as in human diseases and tumorigenesis.25-27, 36 However, the regulatory mechanisms underlying CDR1as functions in HCC remain largely unknown. In our previous studies, we have employed quantitative proteomics-based strategies to screen potential regulatory targets of non-coding RNAs, including a microRNA37 and a long non-coding RNA.38 In this study, we used a similar quantitative proteomic strategy to identify CDR1as-regulated proteins in HepG2 cells. In total, 330 proteins were found to be dysregulated by enhanced CDR1as expression. Bioinformatic analysis showed that these proteins participate in multiple cellular processes, including cell proliferation and the cell cycle. Functional studies found that EGFR, a critical proliferation-related protein and the upstream regulator of mitogen-activated protein kinase (ERK) signaling, contributes to various phenotypic effects observed after CDR1as overexpression. We further showed that CDR1as can inhibit the expression of miR-7, of which EGFR is one of its validated targets. Based on the results of proteomic and functional studies, we provide novel insights into the functions and molecular mechanisms of CDR1as in HCC cells.

Experimental Procedures Cell culture and transfection Cell lines were purchased from the Type Culture Collection of the Chinese Academy of Sciences (Shanghai, China). Cells were cultured in Dulbecco’s modified Eagle’s medium (Gibco, Gaithersburg, USA) containing 10% fetal bovine serum (Gibco) and 100 µg/ml penicillin-streptomycin at 37 °C in 5% CO2. All siRNAs used in this study were ordered from Genepharma Co. Ltd. (Shanghai, China). The plasmid for CDR1as overexpression (pcDNA3-ciRS-7) was a generous gift from Thomas B. Hansen, Aarhus University, Aarhus, 4


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Denmark. The EGFR overexpression plasmid (pCDNA6A-EGFR) was purchased from Addgene. RNAiMAX (Invitrogen, Gaithersburg, USA) and Lipofectamine 2000 (Invitrogen) were used for siRNA transfection and plasmid transfection, respectively. For CDR1as overexpression, cells were transfected with 2 µg pcDNA3-ciRS-7 or control plasmid pcDNA3 (empty vector, EV). For EGFR overexpression, cells were transfected with 3 µg pCDNA6A-EGFR or control plasmid pcDNA6A (empty vector, EV). For EGFR knockdown, cells were transfected with 50 nM EGFR siRNA (siEGFR) or a control siRNA (siNC). Overexpression or knockdown efficiency was confirmed 48 hours (h) post-transfection using quantitative real-time PCR (qPCR). The siRNA and primer sequences are listed in Table S1. Total RNA extraction and qPCR Total RNA from cultured cells was extracted using TRIzol reagent (Invitrogen). The cDNA for qPCR analyses was synthesized using M-MLV Reverse Transcriptase (Promega, Madison, USA) with random primers (Promega). QPCR analysis for genes was performed using SYBR Green PCR Master Mix (Roche, Mannheim, Germany), while miRNA detection was carried out using miDETECT A Track™ qRT-PCR Kit (RiboBio, Guangzhou, China). Gene expression levels were normalized to the housekeeping gene GAPDH, and miRNA expression levels were normalized to U6 (RiboBio). Each sample was analyzed in triplicate on a LightCycler 480 Real-Time PCR system (Roche). The miRNA primers used in this study were purchased from RiboBio. The expression level of CDR1as was measured using divergent primers. The primer sequences are available in Table S1. Cell cycle progression assay Cells were seeded in a 6-well plate at 6×105 cells per well. After transfection, the cells were maintained for 48 h before harvest. Harvested cells were washed with PBS and fixed with 70% ethanol. Cells were stained with PI (Beyotime, Haimen, China) for 30 minutes (min) in the dark, and analyzed with a FACSAria III Cell Sorting System (Becton Dickinson, Bedford, UK) after acquiring 10,000 events for each sample. ModFit LT software (Becton Dickinson) was used to quantify the cell cycle distributions. 5



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Cell proliferation assay The cell proliferation rate was determined using a Cell Counting Kit-8 (CCK-8) (Bossed, Beijing, China). Briefly, transfected cells (5×103/well) were plated into 96-well plates containing 100 µl medium per well. CCK-8 reagents (10 µl) were added to each well at 0 h, 24 h, 48 h and 72 h post-transfection. Then the cells were incubated in a cell culture incubator for 2 h to allow color development. Finally, the absorbance at 450 nm was measured to estimate the number of cells, with 650 nm as reference wavelength. Wound healing assay Cells were grown to confluence on a 6-well plate in complete medium. A straight-lined wound was induced using a 10 µl sterile pipette tip. Debris and loose cells were gently removed using PBS, and DMEM with 1% FBS was added to the plate. Cells were transfected with siRNA or plasmids and allowed to migrate into the wound area. The scratched region was photographed using an inverted microscope equipped with a camera every 24 h after transfection. ImageJ software (National Institutes of Health, Bethesda, USA) was used to estimate the wound area. Cell invasion assay The cell invasion assay was carried out using 24-well Transwell insert chamber plates (Corning Costar, 8.0 µm pore size) coated with Matrigel (BD Biosciences, Franklin Lakes, USA). DMEM containing 10% FBS was added into the lower chamber, and transfected cells (5×104~1×105) in serum-free DMEM were plated into the upper chamber. Cells were cultured in a cell culture incubator for 48 h. After the chamber was dried, invaded cells were fixed and stained with 0.1% crystal violet. Finally, the invaded cells were photographed and quantified.

Cell adhesion assay

The 96-well plates were coated with 10 µg/mL fibronectin (Sino Biological, Beijing, China) and blocked with 1% bovine serum albumin (BSA) in PBS for 2 h at 37 °C. Cells were seeded at 4×105 cells per well in a 6-well plate. After transfection, cells were maintained for 6


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48 h and harvested with trypsin. Harvested cells (1×104/well) were seeded into coated 96-well plates, and incubated for 1 h at 37°C. After non-adherent cells were removed carefully, adherent cells were fixed in methanol for 10 min and stained with 0.1% crystal violet for 20 min. After washing with PBS, 10% acetic acid was added to each well, and then absorbance at 570 nm was measured. Protein extraction and iTRAQ labeling HepG2 cells transfected with 2 µg pcDNA3-ciRS-7 or EV were incubated for 48 h and then harvested. The harvested cells were lysed with RIPA lysis buffer for 15 min on ice and the lysate was centrifuged at 12000 rpm for 30 min at 4 °C. A five-fold excess of ice-cold acetone was added to the supernatant and incubated at -20 °C overnight. The precipitated proteins were centrifuged at 12000 rpm for 30 min at 4 °C. Then, the precipitated proteins were dissolved and the protein concentration was measured with a Bradford protein assay kit (Thermo Fisher Scientific, Bremen, Germany). For each sample, 100 µg of protein was reduced by 10 mM DL-Dithiothreitol (DTT) at 37 °C for 1 h, followed by alkylation with 50 mM iodoacetamide (IAA) for 1 h in the dark and then digestion with Trypsin Gold (Promega) at a ratio of 50 : 1 (w/w, sample: trypsin) at 37 °C for 16 h. The peptides were desalted with a Strata X-C18 SPE column (Phenomenex, Carlsbad, USA) and vacuum-dried. Then the peptides were reconstituted in 0.5 M triethylammonium bicarbonate (TEAB) and labeled using a iTRAQ Reagent-8Plex Multiplex Kit (AB SCIEX, Foster City, USA) according to the manufacturer’s instructions. The resulting tryptic peptides from three biological replicates of the negative control group (EV) were mixed with an equal molar ratio and labeled with 4-plex iTRAQ Tag 113. The resulting tryptic peptides from three biological replicates of the CDR1as overexpressed HepG2 cells were collected and labeled with iTRAQ tags 114, 115, and 116. The labeled peptides were mixed together at an equal molar ratio and dried by vacuum centrifugation. First-dimensional separation of iTRAQ labeled peptides High-pH reverse phase HPLC was employed for peptide fractionation using the Agilent 1200 7



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HPLC System (Agilent Technologies, Palo Alto, USA). The labeled peptide mixture was solubilized in 200 µl of 20 mM ammonium formate (pH 10) and then injected onto narrow bore C18 column (2.1 × 150 mm, 5 µm, Agilent) using a linear gradient of 1% buffer B increase/min from 2-38% of buffer B (buffer A: 20 mM ammonium formate, pH 10, B: 20 mM ammonium formate in 90% acetonitrile, pH 10). The eluted peptides were collected with 1 min interval, and then pooled into 20 fractions, desalted and vacuum dried. LC-MS/MS analysis Peptides from each fraction were solubilized with 2% acetonitrile (ACN) in 0.1% formic acid (FA) and analyzed using an Eksigent nanoLC Ultra 2D plus system (AB SCIEX, Dublin, USA) interfaced with a Triple TOF 5600 System (AB SCIEX, Massachusetts, USA). The peptides were loaded on C18 nanoLC trap column (C18, 100 µm × 3 cm, 3 µm, 150Å) and washed by 2% ACN (0.1% FA) for 10 min at 2 µL/min. An elution gradient of 5-35% ACN (0.1% FA) in 70 min was used on an analytical ChromXP C18 column (C18, 75 µm × 15cm, 3µm 120 Å) with spray tip. The eluate was introduced into the mass spectrometer fitted with a NanoSpray III source (AB SCIEX, Concord, Canada). The parameters for data acquisition were as follows: ion spray voltage=2.5 kV, nebulizer gas = 5 psi, curtain gas = 30 psi, interface heater temperature = 150 °C. For information-dependent acquisition, survey scans were acquired in 250 ms periods, and up to 35 product ion scans were collected as long as they exceeded 150 counts per second. The total cycle time was fixed to 2.5 s. A rolling collision energy setting was used for collision-induced dissociation. The dynamic exclusion was set for 18 s, and the precursor was then refreshed off the exclusion list. Protein identification and quantification ProteinPilot software v5.0 (AB Sciex, Framingham, USA) was used for protein identification and quantitation by searching against the Uniprot human protein database (UP000005640, containing 70615 sequences). Unused score ≥ 1.3 and unique peptides ≥ 2 were set as the criteria for protein identification. Proteins with a |Z score| ≥ 1.96 and P value < 0.05 were considered as differentially expressed proteins (DEPs). Raw data has been deposited in the 8


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PeptideAtlas database (http://www.peptideatlas.org) under the identifier PASS01007 (http://www.peptideatlas.org/PASS/ PASS01007). Western blot analysis RIPA lysis buffer was used for total protein extraction. Lysates were resolved on 12% SDS-PAGE gels and then transferred to PVDF membranes (Millipore, Bedford, USA). The PVDF membrane was blocked with 5% non-fat milk in TBST buffer for 1 h, and then incubated with the primary antibodies (1:1000) at 4 °C overnight. Then, the membrane was washed three times, followed by incubation with anti-mouse or anti-rabbit IgG at a 1:5000 dilution for 1 h at room temperature. Finally, Image Scanner (GE Healthcare Waukesha, USA) was used to detect the antibody-antigen complexes and blot densitometry was conducted using ImageJ software (National Institutes of Health). The polyclonal antibodies for STUB1, SNCA and EGFR were purchased from Abclonal Co. Ltd. (Wuhan, China), and the antibody for GAPDH was purchased from CWBIO Co. Ltd. (Beijing, China). Bioinformatics analysis CDR1as-regulated proteins were categorized using the Protein Analysis Through Evolutionary Relationships (PANTHER) system (http://www.pantherdb.org) based on their function.39 Gene ontology (GO) analysis was carried out using DAVID Bioinformatics Resources 6.8,40 and proteins were classified according to their GO cellular components (CC), biological process (BP) and molecular function (MF). To understand the biological function of the CDR1as-regulated proteins, the DEPs were searched against the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway maps. The protein-protein interaction (PPI) network of CDR1as-regulated proteins was generated using the Search Tool for the Retrieval of Interacting Genes/Proteins database (STRING) v10.0

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with default settings and visualized

using Cytoscape v3.2.1 (http://www.cytoscape.org). For the analysis of CDR1as-interacted miRNAs and their targets, the CDR1as-miRNAs network was predicted using the CircNet database (http://circnet.mbc.nctu.edu.tw/, version of December 16th, 2015).42 The seed sites of miRNA on the CDR1as locus were predicted by 9



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TargetScanHuman (http://www.targetscan.org/). The experimentally validated miR-7 targets were provided in the miRTarBase database (http://mirtarbase.mbc.nctu.edu.tw/).43 Immunofluorescence microscopy Immunofluorescence staining was performed as previously described.38 Briefly, the transiently transfected HepG2 cells were seeded in 35-mm glass-bottom dishes at 37 °C for 48 h, then washed with PBS and fixed with ice-cold 100% ethanol. Subsequently, the cells were permeabilized in 0.5% TritonX-100 and blocked with 10% BSA for 1 h at room temperature (RT). Then, the cells were washed with PBS and incubated with rabbit anti-EGFR polyclonal antibody at a dilution of 1:200 at 4 °C overnight. After washing with PBS, the cells were incubated with Dylight488-conjugated Goat anti-Rabbit IgG (H+L) (Abbkine, Los Angeles, USA) at a dilution of 1:1000 for 1 h at RT in dark. Cells were then stained by ProLong-Gold antifade reagent with 4′,6-diamidino-2-phenylindolein (DAPI). Cells were washed and viewed with an LSM 710 laser scanning confocal microscope (Carl Zeiss, Thornwood, USA). Tissue microarray and RNA-FISH analysis Human HCC tissue microarray slides (Cat: OD-CT-DgLiv04-001) were purchased from Shanghai Outdo Biotech Co., Ltd. (Shanghai, China). A total of 41 matched pairs of HCC samples and the matched non-tumor tissues were obtained with detailed patient information including age, gender, metastasis status, pathological grades, tumor size, TNM stage, and AJCC stage. The human tissue samples were used on the basis of the guidelines of the Nanfang Hospital. Written informed consent forms were signed by each participant prior to his or her inclusion in the study. For RNA-FISH assays, the tissue microarrays were deparaffinized in xylene, rehydrated using decreasing concentrations of ethanol, washed with PBS for 5 min, digested with protein K at 37 °C for 15 min. Sections were incubated in 50% deionized formamide with 2 × SSC for 5min. Next, the tissue microarrays were pre-hybridized in hybridization buffer (25% deionized formamide, 2 × SSC, 10% Dextran sulfate, 2 mM vanadyl ribonucleotide complex, 1 mg/ml yeast transfer RNA, 250 µg/ml N-50 10


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DNA, 0.002 mg/ml nuclease-free bovine serum albumin) at 37 °C for 1 h. After prehybridization, sections were incubated in hybridization buffer containing 250 ng CDR1as probe labeled with Alexa Fluor 647 at 65 °C for 5 min, followed by hybridization at 37 °C overnight in a humidified chamber. The tissue microarrays were washed twice in 2×SSC and 50% deionized formamide for 1 h. After washing with PBS, sections were mounted in ProLong Gold antifade reagent with 4’6-diamidino-2-phenylindole (DAPI) for nucleic acid labeling. The tissue microarrays were washed three times with PBS and scanned using a digital microscopy scanner Pannoramic MIDI (3DHISTECH Ltd., Budapest, Hungary), then quantified and photographed using the Pannoramic Viewer 1.15.2 software (3DHISTECH). The CDR1as probe sequences are available in Table S1. Statistical Analysis For all other experiments carried out in this study, at least three independent biological replicates were performed. Statistical significance between experimental groups was analyzed by Student’s t tests and P < 0.05 was determined to be statistically significant.

Results CDR1as was downregulated in HCC cell lines and HCC tissues We first measured the expression level of CDR1as in two hepatocellular carcinoma cell lines HepG2 and Huh7, an normal human liver epithelial cell line THLE-3, as well as human breast cancer cell line MCF-7, human cervical cancer cell line HeLa and human embryonic kidney cell line 293T. It was shown that cancer cell lines have significantly lower CDR1as expression than normal cell lines (Fig. 1A). CDR1as expression is downregulated in HCC cell lines as compared to normal liver cell lines. We also examined CDR1as expression in human HCC tissues by using HCC tissue microarray analysis (Fig. S1), the results showed that the expression of CDR1as in HCC tissues was significantly lower than that in adjacent liver tissues.

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Functional effects of CDR1as overexpression on HepG2 cells Since CDR1as is downregulated in HCC cell lines and tissues, we used a gain-of-function approach to investigate the function of CDR1as in HCC cells. We examined the overexpression efficiency after transfection of a CDR1as overexpression plasmid (pcDNA3-ciRS-7) and found that CDR1as was consistently upregulated in HepG2 cells even at 7 days post-transfection (Fig. 1B). We further evaluated the functional effects of CDR1as on HCC cells. The CCK-8 assay showed that cell growth was significantly increased after CDR1as overexpression (Fig. 1C). A lower cell migration capacity of HepG2 cells was observed after CDR1as overexpression (Fig. 1D & 1E), whereas the cell invasion ability was unaffected (Fig. 1F & 1G). Meanwhile, overexpression of CDR1as in HCC cells resulted in enhanced cell adhesion to fibronectin (Fig. 1H) and promoted G1/S phase transition (Fig. 1I). Taken together, these data indicated that CDR1as plays a critical role in many fundamental biological processes, including the cell cycle, proliferation and migration, in HCC cells. Furthermore, a similar functional effect of CDR1as was also observed in HeLa cells (Fig. S2). Screening of CDR1as-regulated proteins in HepG2 cells using iTRAQ technology We have demonstrated that CDR1as overexpression could significantly influence the physiological function of HCC cells. However, the molecular mechanism remains largely unknown. Proteomic screening of downstream molecular targets of CDR1as is a highly efficient way to understand its regulatory role. An iTRAQ-based quantitative proteomics strategy was used to globally screen CDR1as-regulated proteins (Fig. 2A). Totally, 6420 proteins were identified (Unused score ≥ 1.3 and unique peptides ≥ 2, Table S2), of which 330 proteins were dysregulated after CDR1as overexpression (|Z score| ≥ 1.96 and P value < 0.05). Among these differentially expressed proteins (DEPs), 133 were downregulated and 197 were up-regulated (Fig. 2B & Table S3). Bioinformatic analysis and validation of CDR1as-regulated proteins To evaluate the biological functions of the CDR1as-regulated proteins, we used the PANTHER classification system to categorize the DEPs. The 280 proteins hit by PANTHER 12


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were categorized into 23 protein classes (Fig. 2C & Table S4). The top five classes were nucleic acid binding (45 proteins), hydrolase (32 proteins), enzyme modulator (29 proteins), transferase (26 proteins) and transcription factor (26 proteins). GO classification analysis demonstrated that the CDR1as-regulated proteins participate in various fundamental biological processes (Fig. S3 & Table S5), indicating the important regulatory function of CDR1as in HCC cells. Moreover, KEGG pathway analysis revealed that these CDR1as-regulated proteins take part in some fundamental biological pathways, including the mTOR and PI3K-Akt pathways (Fig. S4 & Table S6). We further examined the protein-protein interaction of the CDR1as-regulated proteins using the STRINTG database. One hundred and nineteen of the 330 DEPs are involved in a PPI network in STRING database (Fig. 3A & Table S7), suggesting that many CDR1as-regulated proteins tend to interact with each other and work together to exert their functions. Intriguingly, numerous DEPs were functionally related to cell growth and the cell cycle, as indicated in the heat map (Fig. 3B & 3C). Taking these findings together, CDR1as may play important regulatory roles in some fundamental biological processes, especially in regulating cell growth and the cell cycle. Several DEPs with potential regulatory function were selected and validated using Western blotting (Fig. 3D). The Western blotting results were consistent with the iTRAQ experiments, establishing the reliability of the proteomics data. EGFR contributes to the effects of CDR1as overexpression in HCC cells Notably, the epidermal growth factor receptor (EGFR), a validated target of miR-7, was validated to be significantly upregulated after CDR1as overexpression as detected by both Western blot and immunofluorescence staining (Fig. 3D & Fig. S5). To investigate the contribution of EGFR in CDR1as-induced phenotypic changes of HCC cells, we studied the functional effects of EGFR knockdown/overexpression in HCC cells. We determined that overexpression of EGFR significantly increased the proliferation of HCC cells (Fig. 4A & 4K), whereas knockdown of EGFR resulted in a significantly inhibited cell growth (Fig. 4B & 4O). Next, we examined the influence of EGFR expression on cell cycle progress in HCC 13



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cells. It was shown that overexpression of EGFR accelerated the G1/S phase progression (Fig. 4E), while knockdown of EGFR delayed the G1/S phase transition (Fig. 4F). These results showed that the effects of CDR1as overexpression on cell proliferation and cell cycle can be mimicked by manipulating EGFR expression. Therefore, CDR1as may affect cell proliferation and the cell cycle by regulating the expression of EGFR. The effects of EGFR on cell invasion, cell adhesion and migration were also studied. It was shown that overexpression of EGFR could significantly inhibit cell invasion (Fig. 4C & 4D) and enhance cell adhesion (Fig. 4L) of HepG2 cells, while knockdown of EGFR increased the invasion capability (Fig. 4G & 4H) and inhibit cell adhesion (Fig. 4P). However, EGFR dysregulation had no effect on cellular migration (Fig. 4I, 4J, 4M, & 4N). The functional effects of EGFR were also evaluated in another HCC cells line – Huh7 (Fig.5). As expected, similar functional effects were observed. Taken together, our results suggested that CDR1as exerts its effects on HCC cell proliferation and the cell cycle, at least in part, through the regulation of EGFR expression. CDR1as may exert its effects by regulating miR-7 targets in HCC cells Previous studies have shown that circRNAs can act as a miRNA sponge. Here, four miRNAs miR-1225, miR-135a, miR-135b and miR-7, were predicted to interact with CDR1as based on the CircNet database (http://circnet.mbc.nctu.edu.tw/). Figure 6A shows the seed sites of the miRNAs on the CDR1as locus as predicted by TargetScan (http://www.targetscan.org/). We next evaluated the expression levels of the four predicted miRNAs in HepG2 cells after CDR1as overexpression. We determined that miR-7 was significantly downregulated post-transfection, while the other three miRNAs had no significant changes (Fig. 6B). Next, we compared the experimentally validated miR-7 targets with the DEPs identified in the present study. It is intriguing that when CDR1as was overexpressed, fifteen targets of miR-7, including EGFR (predicted by miRTarBase database), were also dysregulated (Fig. 6C & Table S8), implying that CDR1as may exert its function in HCC by regulating miR-7 and its target genes.

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Discussion CDR1as is one of the few well-characterized circRNAs and has emerged as a crucial factor in various diseases and a potential molecular marker for the diagnosis and prognosis of cancers.7, 25

More and more attention has been given to determine its functions and to identify its

regulated genes. With a proteomics approach, we identified 330 proteins regulated by enhanced CDR1as expression in HepG2 liver cancer cells. Among these 330 proteins, very few of them have been reported as CDR1as regulated targets.31, 44-46 Therefore, this proteomic dataset provides a rich source for functional studies of CDR1as in cancer cells. As far as we know, this study serves as the first proteome-wide analysis of a circRNA-regulated protein in cells. Bioinformatic analyses revealed that a large number of DEPs were involved in cell growth and the cell cycle (Fig. 3A), and the epidermal growth factor (EGF) receptor (EGFR) may be the hub of the regulatory networks of CDR1as in HepG2 cells (Fig. 3B). EGFR is an important signaling molecular that can trigger an intracellular signal transduction cascade and regulate cell growth and differentiation.47, 48 It is overexpressed in the majority of solid tumors and play important roles in cancer pathogenesis and progression.49,

50

Clinically, more

aggressive and invasiveness characteristics have been found after EGFR overexpression, and EGFR-mediated malignant behaviors can be interfered with by several strategies, which have already demonstrated potential clinical benefit in the treatment of several tumors.51, 52 More and more preclinical evidence suggests that EGFR-targeted agents could be a new and promising therapy for HCC prevention.53-55 However, the mechanisms involved in the regulation of EGFR function in HCC progression remain elusive. Interestingly, we found that CDR1as can regulate the expression of EGFR and that at least some of the functions of CDR1as are associated with the alteration of EGFR expression in HCC cells. We demonstrated that CDR1as overexpression can promote cell proliferation and induce the S-phase arrest of the cell cycle in HCC cells. By examining the functional effects of EGFR knockdown/overexpression in HCC cells, we showed that the effects of CDR1as overexpression on cell proliferation and the cell cycle can be mimicked by manipulation of EGFR expression. Taking our findings together, we provided evidence that EGFR plays a 15



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critical role in the regulatory network of CDR1as in HCC cells. There is growing evidence of circRNAs acting as miRNA sponges or inhibitors, thereby changing the expression and/or stability of target mRNAs.29, 31, 56, 57 In line with this, we showed that overexpression of CDR1as suppressed miR-7 expression in HCC cells. Consistent with our results, CDR1as was reported to be a “sponge” to inhibit the expression of miR-7,29, 45, 46 and the expression level of miR-7 was inversely correlated with that of CDR1as in HCC tissues.29 MiR-7 has been reported to be an anti-oncomir and plays a crucial role in the cancer cell proliferation, survival, migration and tumor metastasis.30 It can inhibit the expression of oncogenic regulatory factors and function as an anti-oncomir in HCC.29, 34, 35 Thus, inhibition of miR-7 expression induces enhancement of oncogene expression, resulting in tumorigenesis. Intriguingly, miR-7 is known to inhibit EGFR expression, resulting in repressive activity of the downstream EGFR pathway in several cancer cell lines.34, 58-62 In this study, we show that overexpression of CDR1as leads to increased EGFR expression and cell proliferation, as well as reduced miR-7 expression in HCC cells. Our findings suggest that CDR1as regulates cell proliferation by alteration of EGFR signaling through the control of miR-7 expression. Therefore, we characterized the molecular interaction network of the CDR1as/miR-7/EGFR axis in HCC cells. Based on our data, together with previous reports, we propose a model explaining how CDR1as affects cell proliferation and the cell cycle in HCC cells (Fig. 7). We suggest that CDR1as regulates cell proliferation in HCC cells through at least three different pathways. First, as EGFR is an important signaling node that regulates cell growth and differentiation, CDR1as may affect cell proliferation and the cell cycle by regulating EGFR expression. Second, as reported by several groups, CDR1as may promote cell proliferation by regulating hundreds of miR-7 target genes.29, 31, 46 Third, some of the cell growth or cell cycle related proteins listed in Table S3 could provide a mechanism for regulating proliferation and the cell cycle. The outcomes of these pathways can ultimately promote the proliferation of HCC cells, and EGFR plays a key role in CDR1as-mediated regulatory network. However, some phenotypic differences, such as cell invasiveness and migration, exist between CDR1as overexpression and EGFR knockdown/overexpression in HCC cells. Thus, the exact mechanism underlying CDR1as functions may be much more complicated than presently 16


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proposed and some of the other DEPs may be as important as EGFR in the CDR1as-mediated regulatory network. It is worth noting that although CDR1as is a positive regulator of cell proliferation in HCC cells, it is generally downregulated in HCC tissue and HCC cell lines. Consistent with our results, Bachmayr-Heyda et al. reported a negative correlation of global circular RNA abundance and proliferation in colorectal cancer cell lines and cancer tissue.63 And they found that this negative correlation may be a general principle in human tissues as validated in three additional settings. Not only that, they propose an interesting model which can also be used to explain our findings: CircRNAs are more resistant to exonucleases compared to linear RNA, and thus are more likely to accumulate in cells. Cancer cells tend to have higher proliferation rates, during proliferation, CDR1as are equally distributed to daughter cells. So increasing cell proliferation of cancer cells leads to a reduction in circRNAs. In summary, we have carried out the first proteome-wide identification of a circRNA-regulated protein in cells and provided a reliable and high-efficiency method for globally identifying circRNA regulated proteins. By correlating the proteomics data with functional results, we demonstrated that CDR1as exerts its effects on proliferation of HCC cells partly by regulation of EGFR signaling through the control of miR-7 expression. The elucidation of the ciRS-7/miR-7/EGFR axis in the CDR1as-mediated regulatory network provides novel insights into the molecular mechanisms of CDR1as induced-HCC proliferation. It is now important to explore the full significance of the CDR1as-mediated regulatory network in HCC cells. We expect that the proteomics strategy used in this study is applicable to functional studies of circRNA in different cells and organisms.

Financial Disclosure No conflicts of interest, financial or otherwise, are declared by the authors.

Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant No. 31370746), the National Key Research and Development Program (2016YFA0501304), the 17



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Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDB14030202). We thank the Center for Instrumental Analysis and Metrology, Institute of Hydrobiology, Chinese Academy of Science for our Flow Cytometry analysis (Ms. Yan Wang). We also thank The Core Facility and Technique Support, Wuhan Institute of Virology for technique support in digital microscopy scanner Pannoramic MIDI (Ms. Juan Min).

Supporting Information The following additional data are available with the online version of this paper. Table S1: List of siRNA sequences and primer sequences. Table S2: Complete list of the quantified proteins in iTRAQ experiments. Table S3: Complete list of the differentially expressed proteins in iTRAQ experiments. Table S4: Classification of the DEPs using Protein Class ontology. Table S5: Complete list of GO terms enriched in the differentially expressed proteins Table S6: KEGG pathways of the differentially expressed proteins. Table S7: PPI of the differentially expressed proteins. Table S8: Quantified miR-7 targets in miRTarBase database. Figure S1: RNA fluorescence in situ hybridization for CDR1as in HCC tissue microarray. Figure S2: Functional effects of CDR1as overexpression in HeLa cells. Figure S3: GO enrichment analysis of CDR1as-regulated proteins. Figure S4: KEGG pathway enrichment analysis of the CDR1as-regulated proteins. Figure S5: Immunofluorescent staining of EGFR in HepG2 cells after CDR1as overexpression.

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Figure Legends Figure 1. Functional effects of CDR1as overexpression in HepG2 cells. (A) CDR1as expression levels in different cell lines were measured by qPCR. (B) Relative expression level of CDR1as in HepG2 cells after CDR1as overexpression. The effect of CDR1as overexpression on cell proliferation (C), cellular migration (D & E), cell invasion (F & G), cell cycle progression (I) and cell adhesion (H). The white bars represent cells transfected with empty vector (EV), while the black bars refer to the cells transfected with the CDR1as overexpression plasmid (CDR1as). Three independent experiments were performed, and the data are presented as the means ± S.D, *P < 0.05, **P < 0.01. Figure 2. Quantitative proteomic identification of CDR1as-regulated proteins in HepG2 cells. (A) Workflow for quantitative proteomic identification of CDR1as-regulated proteins. Proteins were extracted from the cells 48 h after transfection with EV or CDR1as plasmids. Equal amounts of iTRAQ labeled peptides were mixed for LC-MS/MS analysis. Further functional studies were performed based on the proteomics data and bioinformatics analysis. (B) Heat map showing the DEPs after CDR1as overexpression in HepG2 cells. (C) The DEPs were classified into 23 classes using the PANTHER classification system. Figure 3. Bioinformatic analysis and validation of CDR1as-regulated proteins. (A) The protein-protein interaction (PPI) network of CDR1as-regulated proteins were generated by the STRING database v10.0 and visualized in Cytoscape. The network contains 119 nodes and 137 edges, and the 119 proteins involved in the PPI networks were classified into several functional groups. (B) Heat map showing the expression of 20 DEPs, that were involved in cell growth among all of the CDR1as-regulated proteins. (C) Heat map showing the expression of 25 cell cycle-related proteins after CDR1as overexpression. (D) Validation of the DEPs using Western blotting. GAPDH was used as an internal control. Further densitometric analysis revealed that the results were consistent with the iTRAQ experiments. Figure 4. EGFR contributes to the effects of CDR1as overexpression in HepG2 cells. (A) The mRNA expression of EGFR in HepG2 cells after EGFR overexpression. (B) The mRNA expression of EGFR in HepG2 cells 48 h post-transfection with siEGFR or siNC. (C & G) 23



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The effect of EGFR overexpression or knockdown on cell invasion as determined by a Boyden chamber assay. (D & H) Quantification of the Boyden chamber assay after EGFR overexpression or knockdown. (E & F) Cell cycle analysis after EGFR overexpression or knockdown. (I & M) The effect of EGFR overexpression or knockdown on cell migration. (K & O) The effect of EGFR overexpression or knockdown on cell proliferation. (L & P) The effect of EGFR overexpression or knockdown on cell adhesion. (J & N) Quantification of the wound healing assay. Three independent experiments were performed, and the data are presented as the means ± S.D, *P < 0.05, **P < 0.01. Figure 5. EGFR contributes to the effects of CDR1as overexpression in Huh-7 cells. (A) The mRNA expression of EGFR in Huh-7 cells after EGFR overexpression. (B) The mRNA expression of EGFR in Huh-7 cells 48 h post-transfection with siEGFR or siNC. (C & G) The effect of EGFR overexpression or knockdown on cell invasion as determined by a Boyden chamber assay. (D & H) Quantification of the Boyden chamber assay after EGFR overexpression or knockdown. (E & F) Cell cycle analysis after EGFR overexpression or knockdown. (I & L) The effect of EGFR overexpression or knockdown on cell migration. (K & N) The effect of EGFR overexpression or knockdown on cell proliferation. (J & M) Quantification of the wound healing assay. Three independent experiments were performed, and the data are presented as the means ± S.D, *P < 0.05, **P < 0.01. Figure 6. CDR1as may exert its function by regulating miR-7 targets in HCC cells. (A) Human CDR1as locus and predicted miR-1225, miR-135a, miR-135b and miR-7 target sites are shown. 8mer, 7mer-m8 and 7mer-1A site types are indicated. (B) The expression level of the four miRNAs after EGFR overexpression, as determined by qPCR (C) Heatmap showing the differentially expressed proteins mapped to the experimentally validated miR-7 targets in the miRTarBase database. Figure 7. Proposed model depicting the molecular mechanism of CDR1as in regulating cell proliferation and the cell cycle in HCC cells. CDR1as regulates cell proliferation in HCC cells through at least three different pathways. First, CDR1as may affect cell proliferation and the cell cycle by regulating EGFR expression. Second, CDR1as may promote cell proliferation by regulating hundreds of miR-7 target genes. Third, some of the DEPs related to cell growth or cell cycle could provide new mechanisms for regulating 24


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proliferation and the cell cycle. The outcomes of these pathways can ultimately promote the proliferation of HCC cells, and EGFR plays a key role in CDR1as-mediated regulatory network.

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Figure 1. Functional effects of CDR1as overexpression in HepG2 cells. (A) CDR1as expression levels in different cell lines were measured by qPCR. (B) Relative expression level of CDR1as in HepG2 cells after CDR1as overexpression. The effect of CDR1as overexpression on cell proliferation (C), cellular migration (D & E), cell invasion (F & G), cell cycle progression (I) and cell adhesion (H). The white bars represent cells transfected with empty vector (EV), while the black bars refer to the cells transfected with the CDR1as overexpression plasmid (CDR1as). Three independent experiments were performed, and the data are presented as the means ± S.D, *P < 0.05, **P < 0.01. 98x73mm (300 x 300 DPI)


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Figure 2. Quantitative proteomic identification of CDR1as-regulated proteins in HepG2 cells. (A) Workflow for quantitative proteomic identification of CDR1as-regulated proteins. Proteins were extracted from the cells 48 h after transfection with EV or CDR1as plasmids. Equal amounts of iTRAQ labeled peptides were mixed for LC-MS/MS analysis. Further functional studies were performed based on the proteomics data and bioinformatics analysis. (B) Heat map showing the DEPs after CDR1as overexpression in HepG2 cells. (C) The DEPs were classified into 23 classes using the PANTHER classification system. 108x65mm (300 x 300 DPI)



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Figure 3. Bioinformatic analysis and validation of CDR1as-regulated proteins. (A) The protein-protein interaction (PPI) network of CDR1as-regulated proteins were generated by the STRING database v10.0 and visualized in Cytoscape. The network contains 119 nodes and 137 edges, and the 119 proteins involved in the PPI networks were classified into several functional groups. (B) Heat map showing the expression of 20 DEPs, that were involved in cell growth among all of the CDR1as-regulated proteins. (C) Heat map showing the expression of 25 cell cycle-related proteins after CDR1as overexpression. (D) Validation of the DEPs using Western blotting. GAPDH was used as an internal control. Further densitometric analysis revealed that the results were consistent with the iTRAQ experiments. 121x73mm (300 x 300 DPI)


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Figure 4. EGFR contributes to the effects of CDR1as overexpression in HepG2 cells. (A) The mRNA expression of EGFR in HepG2 cells after EGFR overexpression. (B) The mRNA expression of EGFR in HepG2 cells 48 h post-transfection with siEGFR or siNC. (C & G) The effect of EGFR overexpression or knockdown on cell invasion as determined by a Boyden chamber assay. (D & H) Quantification of the Boyden chamber assay after EGFR overexpression or knockdown. (E & F) Cell cycle analysis after EGFR overexpression or knockdown. (I & M) The effect of EGFR overexpression or knockdown on cell migration. (K & O) The effect of EGFR overexpression or knockdown on cell proliferation. (L & P) The effect of EGFR overexpression or knockdown on cell adhesion. (J & N) Quantification of the wound healing assay. Three independent experiments were performed, and the data are presented as the means ± S.D, *P < 0.05, **P < 0.01. 88x68mm (300 x 300 DPI)



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Figure 5. EGFR contributes to the effects of CDR1as overexpression in Huh-7 cells. (A) The mRNA expression of EGFR in Huh-7 cells after EGFR overexpression. (B) The mRNA expression of EGFR in Huh-7 cells 48 h post-transfection with siEGFR or siNC. (C & G) The effect of EGFR overexpression or knockdown on cell invasion as determined by a Boyden chamber assay. (D & H) Quantification of the Boyden chamber assay after EGFR overexpression or knockdown. (E & F) Cell cycle analysis after EGFR overexpression or knockdown. (I & L) The effect of EGFR overexpression or knockdown on cell migration. (K & N) The effect of EGFR overexpression or knockdown on cell proliferation. (J & M) Quantification of the wound healing assay. Three independent experiments were performed, and the data are presented as the means ± S.D, *P < 0.05, **P < 0.01. 76x65mm (300 x 300 DPI)


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Figure 6. CDR1as may exert its function by regulating miR-7 targets in HCC cells. (A) Human CDR1as locus and predicted miR-1225, miR-135a, miR-135b and miR-7 target sites are shown. 8mer, 7mer-m8 and 7mer1A site types are indicated. (B) The expression level of the four miRNAs after EGFR overexpression, as determined by qPCR (C) Heatmap showing the differentially expressed proteins mapped to the experimentally validated miR-7 targets in the miRTarBase database. 91x34mm (300 x 300 DPI)



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Figure 7. Proposed model depicting the molecular mechanism of CDR1as in regulating cell proliferation and the cell cycle in HCC cells. CDR1as regulates cell proliferation in HCC cells through at least three different pathways. First, CDR1as may affect cell proliferation and the cell cycle by regulating EGFR expression. Second, CDR1as may promote cell proliferation by regulating hundreds of miR-7 target genes. Third, some of the DEPs related to cell growth or cell cycle could provide new mechanisms for regulating proliferation and the cell cycle. The outcomes of these pathways can ultimately promote the proliferation of HCC cells, and EGFR plays a key role in CDR1as-mediated regulatory network. 83x78mm (300 x 300 DPI)


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For TOC Only 106x72mm (300 x 300 DPI)


Quantitative Proteomics Reveals the Regulatory Networks of Circular

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