Regulation of Mirna Biogenesis and Histone Modification by K63-Polyubiquitinated

Author Manuscript Published OnlineFirst on March 15, 2019; DOI: 10.1158/0008-5472.CAN-18-2376 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Regulation of miRNA biogenesis and histone modification by K63-polyubiquitinated

DDX17 controls cancer stem-like features

Shih-Han Kao1,2, Wei-Chung Cheng1,2,3, Yi-Ting Wang5, Han-Tsang Wu6, Han-Yu Yeh1, Yu-Ju

Chen5, Ming-Hsui Tsai7, Kou-Juey Wu1,2,3,4,8,9,10*

1Research Center for Tumor Medical Science, China Medical University, Taichung 404, Taiwan; 2Drug

Development Center, China Medical University, Taichung 404, Taiwan; 3Graduate Institute of Biomedical

Sciences, China Medical University, Taichung 404, Taiwan; 4Institute of New Drug Development, China

Medical University, Taichung 404, Taiwan; 5Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan;

6Department of Cell and Tissue Engineering, Changhua Christian Hospital, Changhua City 500, Taiwan;

7Department of Otolaryngology, China Medical University Hospital, Taichung 404, Taiwan; 8Department of

Medical Research, China Medical University Hospital, Taichung 404, Taiwan; 9Institute of Cellular and

Organismic Biology, Academia Sinica, Taipei 115, Taiwan; 10Cancer Genome Research Center, Chang Gung

Memorial Hospital at Linkou, Taoyuan 333, Taiwan

Correspondence should be addressed to:

Kou-Juey Wu, Research Center for Tumor Medical Science, Institute of New Drug Development,

China Medical University, Taichung 40402, Taiwan; Email: [email protected] or

Downloaded from cancerres.aacrjournals.org on September 30, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on March 15, 2019; DOI: 10.1158/0008-5472.CAN-18-2376 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

[email protected]; Tel: 886-422330114; Fax: 886-422337974

Running Title: DDX17 ubiquitination and Cancer Stemness

Disclosure of Potential Conflict of interest

The authors declare no potential conflicts of interest.

Significance

Hypoxia-induced polyubiquitination of DDX17 controls its dissociation from the pri-miRNA-Drosha-DCGR8 complex to reduce anti-stemness miRNA biogenesis and association with YAP and p300 to enhance transcription of stemness-related genes.

Abstract

Markers of cancer stemness predispose patients to tumor aggressiveness, drug and immunotherapy resistance, relapse, and metastasis. DDX17 is a co-factor of the Drosha-DGCR8 complex in miRNA biogenesis and transcriptional coactivator and has been associated with cancer stem-like properties. However, the precise mechanism by which DDX17 controls cancer stem-like features remains elusive. Here we show that the E3 ligase HectH9 mediated

K63-polyubiquitination of DDX17 under hypoxia to control stem-like properties and tumor-initiating capabilities. Polyubiquitinated DDX17 disassociated from the Drosha-DGCR8 complex, leading to decreased biogenesis of anti-stemness miRNAs. Increased association of polyubiquitinated-DDX17 with p300-YAP resulted in histone 3 lysine 56 (H3K56) acetylation proximal to stemness-related genes and their subsequent transcriptional activation. High expression of HectH9 and six stemness-related-genes (BMI1, SOX2, OCT4, NANOG, NOTCH1, and NOTCH2) predicted poor survival in patients with HNSCC and lung adenocarcinoma. Our findings demonstrate that concerted regulation of miRNA biogenesis and histone modifications through post-translational modification of DDX17 underlies many cancer stem-like features.

Inhibition of DDX17 ubiquitination may serve as a new therapeutic venue for cancer treatment.

Introduction

Cancer stem-like cells (CSCs) possess the ability to self-renew and differentiate into mature tumor cells inside a tumor mass. These stem-like features of CSCs are associated with higher tumorigenicity, drug response, relapse, and metastasis in cancer patients (1). Recently, stemness features, which were extracted from transcriptomic and epigenetic signatures and identified by machine learning algorithm, have also shown correlation with immunotherapy response (2).

Understanding the mechanisms leading to stemness is thus vital for treating cancers.

Among the proteins that regulate pluripotency and reprogramming are the DEAD-box

RNA-binding proteins (RBPs) (3). DEAD-box RBPs have critical roles in RNA metabolic pathways, including transcription, splicing, miRNA biogenesis, translation and decay (4). One important DEAD-box RBP is DDX17, which is a known co-factor of the Drosha Microprocessor and facilitates recognition and processing of primary miRNAs (pri-miRNAs) to become mature miRNAs (5). The Microprocessor is mainly constituted of Drosha (a ribonuclease) and DGCR8 (a double-stranded RNA binding protein) (6). However, different co-factors, such as DDX5 and

DDX17, hnRNP A1, and BRCA1 can associate with the Microprocessor to modulate its activity

(7–10). Depending on their downstream targets, miRNAs may function as oncogenes or tumor suppressor genes (11). In human cancers, sequestration of DDX17 by YAP in the Hippo pathway has been reported to cause global downregulation of miRNAs (9). DDX17 also acts as a

transcriptional co-activator for a range of transcription factors, which cause tumorigenicity in cancers (12,13).

Interestingly, DDX17 and other RBPs can be regulated by post-translational modifications

(PTMs), which can change RNA-binding affinity, function, and localization, thereby contributing additional layers of regulatory complexity (14). PTMs in the form of ubiquitination via K63 linkage, function as a platform for protein-protein interactions (14). HectH9 is one of the few E3 ligases that can trigger both K63- and K48-linked ubiquitination (15) and has been implicated in the maintenance and development of stem cells (16) and tumor progression (17). HectH9 is overexpressed in various cancers (17) and has been reported to be a potential target for anti-cancer therapies (18).

In locally advanced solid tumors, hypoxia is an important micro-environmental factor that leads to malignant progression by increasing tumor stemness (19). Several stemness-related genes, whose expressions rely on the orchestration of epigenetic signatures and mRNA/miRNA post-transcriptional networks (2), are required to program either embryonic stem cells or tumor cells into pluripotency under hypoxia (20). Therefore, hypoxia is a suitable testing model to demonstrate the regulation of cancer stemness.

Several unanswered questions remain with regard to inducing stem-like properties in cancer cells. First, the detailed mechanism by which DDX17 promotes stem-like properties is unknown.

Second, it is unclear whether DDX17 could be ubiquitinated and whether the ubiquitination site

could control stem-like characteristics, which, in turn, affect therapeutic efficacy in cancer treatment. Finally, DDX17 is an important factor in both transcriptional activation and miRNA biogenesis (21), however, the question remains which of these two machineries, transcriptional or post-transcriptional or both, regulates cancer stem-like properties.

Here we present a unifying model that K63-polyubiquitinated DDX17 can control miRNA biogenesis and histone modification to induce the expressions of stemness-related genes. This study uncovers a precise mechanism by which a RBP plays a central role in coordinating post-transcriptional and transcriptional machineries to define cancer stem-like features and provides a therapeutic venue for targeting ubiquitination of a RBP.

Materials and Methods

Cell culture and oxygen deprivation. Human head and neck cancer FaDu and OECM-1, lung cancer H1299, and breast cancer MCF7 cells were obtained from ATCC and MOR/CPR lung cancer cell line from the European Collection of Cell Cultures (ECACC). Cell line authentication was done by Food and Industry Research & Development Institute (Taiwan) using short random repeat DNA sequencing. The human embryonic kidney 293T cell line was obtained as described

(22). One primary cultures of HNSCC cells were kindly provided Dr. M.-H. Yang (National

Yang-Ming University) (23). Head-and-neck cancer stem cells (with ALDH1+, CD44+ and CD133+) were purchased from Celprogen (CA, USA). The cell lines showed negative results for

mycoplasma contamination. Oxygen deprivation was performed in an incubator with 1% O2, 5%

CO2, and 94% N2 for 18 hr.

Plasmids and stable transfection. The pDESTmyc-DDX17 plasmid was purchased from

Addgene. The pCM6-Myc-DDK-DDX17, the OCT4, SOX2, and BMI1 promoter-driven luciferase reporter constructs were purchased from OriGene Technologies, Inc. The p300 construct was from

Dr. Wei-Yi Chen. The HectH9 and pHA-HIF-1-(ODD) plasmids were obtained from Prof. Wei.

Gu (Columbia Univ.) and Dr. L.E. Huang (Univ. of Utah), respectively. The pCMV-MIR-34a, pMirTarget-BMI1 3’UTR, pMirTarget-SOX2 3’UTR, and pMirTarget-OCT4 3’UTR plasmids were purchased from OriGene (MD, USA). pXP-Huwe1 promoter was constructed using HindIII and

BglII sites. The pcDNA3.1-MIR-16-1, pLKO.AS2-HIF-1-(ODD) and mutated plasmids in

3’UTR and in pXP-Huwe1 promoter were commercially synthesized and cloned (GENEWIZE,

NJ). The oligonucleotides used to generate various plasmids and the methods to construct these plasmids were shown in Supplementary Table S1 and S2. The generation of stable clones by calcium phosphate transfection was described (22). All the stable clones were established by transfection of plasmids as designated by the name of the clones.

Protein extraction and Western blot analysis. The extraction of proteins from cells and Western blot analysis was performed as described (22). The characteristics of the antibodies used were listed in Supplementary Table S3.

RNA extraction and quantitative real-time PCR. RNA purification, cDNA synthesis and quantitative real-time PCR analysis were performed as described (24). For miRNA, RNA was extracted by TRIZol (Invitrogen). Mature miRNA expressions were quantified by the TaqMan miRNA assay (Applied Biosystems). Pri-miRNA expressions were analyzed by qPCR using Fast

SYBR Green Master Mix (Applied Biosystems). The sequences of primers used in the real-time

PCR experiment were shown in Supplementary Table S4.

In Vivo Limiting Dilution Assay. The in vivo limiting dilution assay were performed as described

(22). At least six mice were used for each group of experiment. Briefly, cells were suspended in 50

μl of DMEM at the indicated densities and were mixed with an equal volume of Matrigel (Corning,

USA). The mixture was subcutaneously injected into the lateral flanks of 6-week-old female

Balb/c nude mice. Mice were sacrificed 8 weeks after inoculation of tumor cells. The tumor weights were measured after harvesting the xenotransplantation. All animal studies were approved by the Institutional Animal Care and Use Committee (IACUC), China Medical University

(Reference number: 2017-042).

Quantitative Chromatin Immunoprecipitation. Cells were cross-linked with 1% formaldehyde for 10 min and stopped by glycine to a final concentration of 0.125 M (24). Fixed cells were washed twice with Tris buffered saline (20 mM Tris, pH 7.5, 150 mM NaCl) and harvested in 5 ml of SDS buffer (50 mM Tris, pH 8.0, 0.5% SDS, 100 mM NaCL, 5 mM EDTA and protease inhibitors). Cells were pelleted by centrifugation and suspended in 2 ml of IP buffer (100 mM Tris, pH 8.6, 0.3% SDS, 1.7% Triton X-100, 5 mM EDTA). Cells were sonicated with a 0.25-inch diameter probe for 15 s twice using a Bioruptor® Pico sonicator (Diagenode, Belgium). For each immunoprecipitation, 1 ml of lysate was precleared by adding 50 μl of blocked protein A beads

(50% protein A-Sepharose, Amersham Biosciences; 0.5 mg ml-1 bovine serum albumin, 0.2 mg ml-1 salmon sperm DNA) at 4 °C for 1 h. Samples were spun, and the supernatants were incubated at 4 °C for 3 h with no antibody, IgG or antibodies to be tested. Immune complexes were recovered by adding 50 μl of blocked protein A beads and incubated overnight at 4 °C. Beads were successively washed with (1) mixed micelle buffer (20 mM Tris, pH 8.1, 150 mM NaCl, 5 mM

EDTA, 5% w/v sucrose, 1% Triton X-100, 0.2% SDS), (2) buffer 500 (50 mM HEPES, pH 7.5,

0.1% w/v deoxycholic acid, 1% Triton X-100, 500 mM NaCl, 1 mM EDTA), (3) LiCl detergent

wash buffer (10 mM Tris, pH 8,0.5% deoxycholic acid, 0.5% Nonidet P-40, 250 mM LiCl, 1 mM

EDTA), and (4) TE buffer (10 mM Tris, 1 mM EDTA) and then eluted with 1% SDS and 0.1 M

NaHCO3. 20 μl of 5 M NaCl was added to the eluates, and the mixture were incubated at 65 °C for

4 h to reverse the cross-linking. After digestion with proteinase K, the solution was phenol/chloroform-extracted and ethanol-precipitated. DNA fragments were resuspended in 50 μl of water and 5 μl was used in a PCR reaction. For qChIP assay, DNA samples were quantified by the SYBR Green assay using SYBR Green PCR Master Mix (Applied Biosystems) with specific primer. Data were analysed by the CT method and plotted as % input DNA. qChIP values were calculated by the following formula: % input recovery = [100/(input fold dilution/bound fold dilution)] × 2(input CT-bound CT). The antibodies used are listed in Supplementary Table S3. The primers used in qChIP assay are listed Supplementary Table S5.

In vitro sphere formation. Cells (1 x 103) were seeded and incubated with serum-free medium composed of DMEM/F12 (Thermo Fisher Scientific, USA), N2 supplement (Thermo Fisher

Scientific, USA), B-27 supplement (ThermoFisher Scientific, USA), 10 ng ml-1 human recombinant basic fibroblast growth factor (bFGF) (ThermoFisher Scientific, USA) and 10 ng ml-1 epidermal growth factor (EGF) (ThermoFisher Scientific, USA) in a 12-well plate. The spheroids were resuspended to form secondary and tertiary spheroids. Tumor spheres with a size > 100 m were counted. The number of spheroids was counted after 14 days.

In vitro clonogenic assay. Briefly, cells (2 x 103) were seeded in 0.6% agarose supplemented with

10% FBS DMEM. After 3 weeks of incubation, colonies were fixed with 3.7% paraformaldehyde for 30 min at RT and stained by 0.01% crystal violet.

Flow cytometry analysis and sorting. To determine the ALDH1 expression, cells were stained with the ALDEFLUOR assay kit (Stem Cell Technologies. A specific ALDH1 inhibitor, diethylaminobenzaldehyde, was used as a negative control, and cells were analyzed with a

FACSCalibur flow cytometer. 1 x 107 cells were stained with the ALDEFLUOR assay kit and subjected to sorting by BD FACSAriaTM III (BD Biosciences, USA).

Luciferase reporter assays. The reporter constructs of BMI1-3’UTR, SOX2-3’UTR, and

OCT4-3’UTR and their mutated constructs were transfected into H1299/WT and H1299/K190R stable cells or 293T/scri-si and 293T/HectH9-si stable cells. pMIR expression vectors were co-transfected with DDX17-WT or DDX17-K190R expression plasmids into 293T cells accordingly. The reporter constructs of BMI1-promoter, SOX2-promoter, and OCT4-promoter were co-transfected with DDX17-WT or DDX17-K190R expression plasmids into 293T cells. A pCMV--gal plasmid was used as an internal control. The luciferase activities were assayed as described (22).

Lentivirus siRNA and cDNA overexpression experiments. Lentivirus containing short hairpin

RNAs (shRNAs) expressed in a lentiviral vector (pLKO.1-puro) or containing designated cDNAs were generated in 293T cells as previously described (25). The sequence and clonal names of plasmid pLKO-scrambled or other pLKO plasmids against HectH9 and DDX17 were described in

Supplementary Table S6. These LKO plasmids and packaging plasmid pCMVΔR8.91 and pMD.G were provided by National RNAi Core Facility of Academia Sinica (Taipei, Taiwan). For lentivirus production, 293T cells were transfected with 5 μg pLKO.1-puro lentiviral vectors expressing different shRNAs along with 0.5 μg of envelope plasmid pMD.G and 5 μg of packaging plasmid pCMVΔR8.91, whereas for overexpression, the amounts of plasmids were doubled at transfection.

Virus was collected 48 hours after transfection. To prepare HectH9 or DDX17 knockdown/overexpression cells, H1299, OECM-1, or FaDu cells were infected with lentivirus for

24 h, and stable clones were generated by selection with appropriate antibiotics.

Isolation of K63 ubiquitinated proteins. K63-ubiquitinated proteins were isolated from cell lysate using Flag K63-TUBE (K63-Tandem Ubiquitin Binding Entities; Life Sensors, cat. No.

UM604). The experimental process was conducted according to the manufacturer’s protocol.

Briefly, to pull down proteins, cells were lysed with standard lysis buffer with additional 100 M

K63-TUBE peptide, 100 M PR619, 2.5 mM -PA and 5 mM NEM. The cell lysate was incubated for 2 hours at 4°C with the TUBE peptide. K63 proteins were immunoprecipitated using Flag-M2 agarose affinity beads at 4°C overnight. Ubiquitinated proteins were eluted 0.2 M glycine HCl, pH

2.5 for 1 hr at 4°C. The pH of the eluate was neutralized by 1 M Tris, pH8.0 for proteomic analysis.

One-tenth of the eluates were boiled in Laemmli buffer for western blotting.

In-Gel Digestion. The protein samples from SDS-PAGE were subjected to typical in gel digestion.

The gels were cut into small gel pieces and washed 3 times with 25 mM TEABC containing 50%

(v/v) ACN followed by dehydrating with 100% ACN and completely drying by vacuum centrifugation. Then, protein samples were digested by Trypsin (protein:trypsin= 50:1, g/g) in 25 mM TEABC at 37°C overnight. The extraction of tryptic peptides was performed 3 times with 5%

(v/v) FA in 50% (v/v) ACN for 30 min and dried completely by vacuum centrifugation at room temperature.

LC-MS/MS Analysis. Peptide were reconstituted in buffer A (0.1% formic acid) and analyzed by using LTQ-Orbitrap Fusion mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) with a nanospray interface. Peptide samples were directly loaded onto a 75 μm × 250 mm Thermo

Scientific Acclaim PepMap100 C18 Nano LC Column. The mass spectrometer was operated in the data-dependent mode with a Top speed method. Tandem MS was performed with an isolation window of 1.6 Da using quadrupole and CID fragmentation with collision energy of 30. The second biological replicates were analyzed using the TripleTOF 5600 system (AB SCIEX) with a nanoACQUITY UPLC (Waters) were applied with 15-cm × 100 μm self-pulled column packed with C18-AQ particles (Dr. Maisch). Peptides were separated through a gradient of up to 80%

(vol/vol) buffer B (acetonitrile with 0.1% formic acid) over 120 min at the flow rate of 500 nL min-1. Data were acquired using data dependent mode that top 10 precursor ions were selected based on exceeding a threshold of 100 cps in each MS survey scan, and 10 MS/MS scans were performed for 200 ms each. The collision energy was adjusted automatically by the rolling CID function of Analyst TF 1.5 and dynamic exclusion was set at 6 second.

Protein Identification and quantitation by MaxQuant. The raw files were processed with

MaxQuant (version 1.5.1.11) and MS/MS spectra were searched by Andromeda search engine against the SwissProt human database (Jun. 2014) with the following parameters were allowed: tryptic peptides with 0 to 2 missed cleavage sites; the parent ion tolerance was 10 ppm and the fragment ion mass tolerance was 0.6 Da; oxidation (M) was set as variable modifications. Search results were processed with MaxQuant and filtered with a false discovery rate of 0.01. Label free quantitation (LFQ) were obtained through MaxQuant. The match between run option and the

LFQ quantitation were activated.

Co-immunoprecipitation assays and GST pull-down assays. Co-immunoprecipitation experiments were performed by incubating different antibodies (Supplementary Table S3) overnight with 0.5-1 mg of whole cell lysates prepared by lysis in 150 mM NaCl, 1% Nonidet

P-40, 1% deoxycholate, 0.1% sodium dodecyl sulfate (SDS), 50 mM Tris-HCl (pH 7.5), 1 mM

PMSF, 25 mM NaF and protease inhibitors, from H1299, MCF7, and 293T cells overexpressing proteins of interest. The immune complexes were incubated with protein-A/G beads at 4℃ for 2 hr, preblocked with 10% bovine serum albumin (BSA). The immunoprecipitates were washed three times with the same lysis buffer, mixed with 1× Laemmli dye, boiled for 5 min, and loaded on

SDS-polyacrylamide gels (SDS-PAGE). After transfer, the filters were blocked with blocking buffer, probed with primary and secondary antibody sequentially and developed. The GST pull-down assay was performed by incubating Flag-DDX17-WT, K63-Ub-Flag-DDX17-WT or

Flag-DDX17-K190R protein with GST-p300-CH3-WT or GST-p300-CH3-ΔZZ protein (or GST protein as a control) and Glutathione-Agarose (Sigma, St. Louis, MI, USA).

K63-Ub-Flag-DDX17-WT protein was obtained by co-transfecting Flag-DDX17-WT with

HA-ubiquitin-K63 plasmids into 293T cells. All the Flag constructs were purified by anti-Flag M2 agarose and eluted by 3x Flag peptides. Purification of GST proteins was performed as previously described (24). The pulled down Flag-DDX17 protein was detected by Western blot analysis.

Identification of miRNA targeting the stemness-related genes. The validated and predicated miRNA-gene relationships in this study were based on the pipeline constructed in our previous study (26). In brief, data of experimentally-validated miRNA-gene interactions were extracted and identified from miRTARBase (27), a curated database of miRNA-target interactions. For predicted miRNA-gene interactions, 12 bioinformatics tools were used to identify putative miRNAs targeting stemness-related genes. The miRNAs predicted to target stemness-related genes

(or anti-stemness miRNAs) by at least 3 algorithms were selected for further experimental validation. As shown in Fig. 4A and Supplementary S4A, 11 miRNAs are identified to target the 6 stemness-related genes.

H3K56Ac ChIP-seq data analysis. The H3K56Ac ChIP-seq dataset was obtained from Roadmap

Epigenomics Project at Gene Expression Omnibus (GSE16256). Raw sequencing reads were mapped to the human genome (assembly hg19) using Bowtie v0.12.7 algorithm with m1 and v3 parameters (28). The binding regions of H3K56Ac were identified by MACS v1.4.2 algorithm (29)

with default parameter and then annotated by the Bioconductor package, ChIPpeakAnno (30).

Survival Analysis. Published microarray data of series from Gene Expression Omnibus (31)

(GSE10300, GSE2837, GSE42127, GSE14814) were used to obtain probe-level expression values for HectH9 and the six stemness-related genes in HNSCC (n=84) and lung adenocarcinoma

(n=204). We used Jetset (32) to select the optimal microarray probe set to represent a gene for those with multiple probes and used variance normalized values (z-score) to indicate the gene expression levels. For survival analysis, each HNSCC or lung cancer was classified as positive of the HectH9-stemness signature if HectH9 and the sum of the z-scores for the expression values of

6 stemness genes were greater than the median of the population. The Kaplan-Meier estimate was used for survival analysis, and statistical significance for differences between survival curves of patients was determined using the log-rank test by the survival package (33) of R (version 3.4.0)

(34).

Statistical analyses. Unless otherwise noted, each sample was assayed in triplicate. For in vitro analyses, each experiment was repeated at least three times. All error bars represent s.e.m. and the exact n is stated in the corresponding figure legend. Student’s t test was used to compare two groups of independent samples. The level of statistical significance was set at 0.05 for all tests.

Data Availability. All of the mass spectrometry derived proteomics datasets have been submitted in the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the Jpost partner repository with the dataset identifier JPST000240 and PXD006059. The data that support

the findings of this study are available from the corresponding author upon request.

Results

HIF-1α-induced HectH9 Confers Cancer Stemness-like Properties in Tumor Cells

Since hypoxia-induced pluripotency is a good model to explore the underlying mechanisms that contribute to cancer stemness-like features (20), we decided to utilize this experimental system. Due to the overlapping role of hypoxia and HectH9 in regulating cancer stemness (16,20), we tested whether HectH9 expression is induced by hypoxia. We found that hypoxic treatment in several tumor cell lines (including head-and-neck: FaDu and OECM-1, lung: H1299, and breast:

MCF7) and overexpression of a constitutively-active HIF-1α(ΔODD) construct in 293T cells led to an up-regulation of HectH9 mRNA and protein levels (Fig. 1A and Supplementary Fig. S1A).

Promoter characterizations showed that hypoxia-response element (HRE) mutation at -340~-336 of HectH9 promoter abolished activation of HectH9 expression in a reporter gene assay, suggesting that HIF-1α could transcriptionally regulate HectH9 expression (Supplementary Fig.

S1B). To investigate whether HectH9 plays a role in mediating tumor stemness, we knocked down

HectH9 expression in OECM-1 and H1299 cells and conducted the tumor sphere assays (Fig. 1B and Supplementary Fig. S1C). Suppression of HectH9 significantly decreased the number of spheres and colonies (Fig. 1, C and D; Supplementary Fig. S1, D and E). HectH9 knockdown mildly decreased proliferation without a change in apoptosis (Supplementary Fig. S1, F and G), suggesting that proliferation and apoptosis may not be the main causes for reduced colony

numbers. Flow cytometry analyses revealed that HectH9 knockdown reduced ALDH1+ population

(Fig. 1E and Supplementary Fig. S1H), which represent the stem-like population (35). Next, to evaluate whether HectH9 plays a role in inducing stem-like subpopulation of cancer cells, we suppressed HectH9 expression in ALDH1- and ALDH1+ cells. Sphere formation in ALDH1+ cells was impaired by HectH9 knockdown compared to controls. (Supplementary Fig. S1, I and J).

The impact of HectH9 on stem-like traits was also validated in the primary culture of head-and-neck squamous cell carcinoma (HNSCC) cells and primary head-and-neck cancer stem cells (CSC) as HectH9 knockdown reduced the sphere numbers of these tumor cells

(Supplementary Fig. S1, K-M).

We further examined the role of HectH9 in regulating HIF-1α-induced tumor stemness. We knocked down HectH9 in HIF-1α(ΔODD)-expressing clones and found that while HIF-1α(ΔODD) increased the sphere numbers, knockdown of HectH9 significantly decreased the sphere numbers

(Fig. 1, F-H). The tumor-initiating ability was compromised in vivo when HectH9 was knocked down in HIF-1α(ΔODD)-expressing FaDu cells at 103-cell inoculation (Fig. 1, I and J;

Supplementary Table S7). Together, these data indicate that HIF-1α could induce HectH9, whose expression regulates tumor stem-like property and tumor-initiating capability.

K63-linked Polyubiquitination of DDX17 Mediated by HectH9

Because HectH9 is an E3 ligase involved in K63-linked poly-ubiquitination (15,24), we

hypothesized that HectH9-mediated tumor stemness requires the enzymatic activity and an effector substrate. To characterize possible substrates of HectH9, we immunoprecipitated K63-linked ubiquitinated proteins with Flag-tagged K63-specific tandem ubiquitin-binding entity (K63-UIM)

(36) in H1299/HectH9-si and H1299/scr-si cells and identified them by liquid chromatography tandem-mass spectrometry (LC-MS/MS) (Fig. 2A and Supplementary Fig. S2A). Among the 29 proteins identified in replicates, 72% of the putative substrates belong to the RNA metabolic process pathway by Gene Ontology (http://www.geneontology.org/) (Fig. 2B and Supplementary

Table S8). We chose DDX17 for further characterizations since DDX17 is an RNA helicase and plays a significant role in RNA metabolism that may regulate microRNA processing and transcription (5,9,21). Supporting the rationale to test tumor stem-like property regulated by

DDX17, we found that suppression of DDX17 attenuated the number of sphere formation in both

H1299 and OECM-1 cells (Supplementary Fig. S2, B-D). In accordance with HectH9 suppression, DDX17 knockdown slightly reduced proliferation but did not change apoptosis

(Supplementary Fig. S2, E and F). To test whether DDX17 is a bona fide substrate of HectH9, we overexpressed HectH9-wildtype or catalytically-inactive HectH9 (HectH9-CS) (37) and found increased DDX17 polyubiquitination with the wild-type HectH9 but not with HectH9-CS

(Supplementary Fig. S2G). Co-immunoprecipitation experiments revealed that HectH9 and

DDX17 could endogenously interact with each other in vivo (Fig. 2C). To corroborate the ubiquitination status of DDX17 by HectH9 under hypoxia, we immunoprecipitated DDX17 and

found that hypoxia induced K63-linked polyubiquitination of DDX17, which was decreased when

HectH9 was suppressed, whereas K48-linked polyubiquitination moieties on DDX17 were not perturbed (Fig. 2D), suggesting that hypoxia-mediated HectH9 does not control DDX17 protein degradation. Indeed, the cycloheximide chase assay showed that DDX17 was a stable protein under HectH9 suppression (Supplementary Fig. S2H). Collectively, these data demonstrate that

DDX17 is K63-linked polyubiquitinated by HectH9 under hypoxia.

Next, to identify the poly-ubiquitination site on DDX17 mediated by HectH9, we truncated

DDX17 into DDX17-C and DDX17-N and co-expressed them with V5-HectH9 and HA-Ub plasmids in 293T cells. We found that the polyubiquitination moieties were mainly located at the

N-terminal fragment of DDX17 encompassing the helicase domain under HectH9 overexpression

(Supplementary Fig. S2I). Further truncation of the N-terminal fragment indicated that ubiquitination of fragment 101-200 was elevated by either HectH9 overexpression

(Supplementary Fig. S2J) or hypoxia (Supplementary Fig. S2K). Individual lysine residue mutation showed that only Flag-DDX17-101-200K190R abolished the HectH9-mediated polyubiquitination signal (Supplementary Fig. S2L). To validate the polyubiquitination status under hypoxia, we co-expressed full-length DDX17-WT or DDX17-K190R with two mutants of ubiquitin (K48R or K63R) followed by hypoxic exposure. We found that only K48R-ubiquitin was ligated to DDX17-WT but not to DDX17-K190R under hypoxia (Fig. 2E, left). On the contrary, the K63R-ubiquitin mutant could not form the ubiquitin chains on either DDX17-WT or

DDX17-K190R (Fig. 2E, right).

We next generated DDX17-WT and DDX17-K190R-expressing stable clones by lentivirus in

H1299 and FaDu cells (Supplementary Fig. S2M). Cells with K190R showed significantly reduced sphere and colony formation abilities compared to WT (Fig. 2, F and G), with a slight decrease in proliferation and no difference in apoptosis (Supplementary Fig. S2N and O).

Therefore, these data suggest that DDX17 poly-ubiquitination at K190 may play an important role in HectH9-mediated tumor stem-like properties.

Non-ubiquitinated DDX17-K190R Reduces Cancer Stemness Properties and

Tumor-Initiating Capabilities

To further characterize the downstream effectors in stemness, we screened a group of stemness-related genes associated with hypoxia (20,38) in HectH9- and DDX17-knockdown cells.

OCT4, SOX2, NANOG, BMI1, NOTCH1, and NOTCH2 were concurrently downregulated but not

WNT1 (Fig. 3A and Supplementary Fig. S3A), suggesting that OCT4, SOX2, NANOG, BMI1,

NOTCH1, and NOTCH2 may be the downstream targets of HectH9 and DDX17. We next confirmed that these six stemness-related genes were upregulated at the RNA and protein levels under hypoxia. (Fig. 3B; Supplementary Fig. S3, B and C). Knockdown of each of OCT4, SOX2,

NANOG, BMI1, NOTCH1, and NOTCH2 gene reduced the sphere formation (Supplementary Fig.

S3D), indicating that these six genes are important in driving cancer stemness in our cell line

model.

Next, we examined the effect of ubiquitination of DDX17 on these stemness-related genes in

WT or K190R stable clones in H1299 or FaDu cells. The RNA levels of OCT4, SOX2, NANOG,

BMI1, NOTCH1, and NOTCH2 were decreased in K190R compared to WT, respectively (Fig. 3, C and D). The protein levels of these stemness-related genes in K190R cells were also reduced in accordance with their RNA levels (Fig. 3, E and F). Flow cytometry revealed that K190R clones possessed less ALDH1+ population than WT cells (Fig. 3, G and H). The in vivo tumor initiation study showed that FaDu/K190R reduced tumor development at low-dose (103) implantations compared to the wild-type counterpart (Fig. 3, I and J; Supplementary Table S7). Since the stem-like feature is associated with drug resistance in cancer (39), we investigated whether HectH9 or DDX17 ubiquitination would affect drug sensitivity in cisplatin-resistant MOR/CPR cells. The data showed that HectH9 knockdown or K190R overexpression in MOR/CPR cells increased cisplatin sensitivity by inducing apoptosis (Fig. 3, K and L; Supplementary Fig. S3E-J), implicating that the HectH9-DDX17 axis might be the therapeutic targets in cancer treatment.

DDX17 Ubiquitination Regulates miRNAs Biogenesis Targeting Stemness-related Genes

Since DDX17 plays a role in microRNA biogenesis (9), which is involved in tumor stemness

(40), we examined whether HectH9-mediated polyubiquitination of DDX17 can alter the activity of miRNA biogenesis. We utilized bioinformatics approaches to identify the

experimentally-validated and predicted miRNA-gene interactions of the six stemness-related genes

(the detailed information is described in Method, Fig. 4A and Supplementary Fig. S4A). The activity of the Microprocessor can be determined by comparing the amount of pri-miRNAs with that of mature miRNAs (9). Quantification by qRT-PCR showed that representative mature miRNAs were repressed under hypoxia (Fig. 4B, left panel; Supplementary Fig. S4, B and C, left panel), with corresponding sustained or accumulated pri-miRNA expression (Fig. 4B, right panel; Supplementary Fig. S4, B and C, right panel), indicating that hypoxia reduces the activity of the Microprocessor. To examine whether the hypoxia-reduced activity of the

Microprocessor is mediated by the HectH9-DDX17 pathway, we next characterized the miRNA status in HectH9-expressing cells. Similar to hypoxia-treated cells, HectH9 overexpression consistently reduced mature miRNA levels with sustained or accrued pri-miRNA levels (Fig. 4C).

To demonstrate that these microRNAs play a role in HectH9-mediated tumor stem-like characteristics, we replenished miR-16 or miR-34a in HectH9-overexpressing cells as an example and found that overexpression of miR-16 or miR-34a decreased the sphere and colony numbers in

HectH9-overexpressing cells (Supplementary Fig. S4, D-G). On the contrary, suppression of

HectH9 increased mature miRNA levels without concordant changes at the pri-miRNA levels

(Supplementary Fig. S5A) and further knockdown of miR-16 or miR-34a increased the sphere and colony numbers in HectH9-knockdown cells (Supplementary Fig. S5, B-E). These data suggest that HectH9 can modulate hypoxia-induced tumor stem-like traits via miRNA biogenesis.

Next, to characterize whether DDX17 ubiquitination regulates miRNA biogenesis, we examined the expression levels of the representative miRNAs in DDX17-WT and DDX17-K190R cells. We found that non-ubiquitinated DDX17 mutants significantly induced more mature miRNA productions compared to the wild-types, without concordant changes at the pri-miRNA levels (Fig.

4D). Antagonizing miR-16 or miR-34a expression resulted in the increase in sphere and colony formation in K190R cells (Supplementary Fig. S5, F-I).

To demonstrate the posttranscriptional effect of DDX17 ubiquitination, we examined the luciferase activities harboring the 3’UTR of three representative genes, BMI1, SOX2, and OCT4, in

DDX17-WT and K190R cells. The activities of BMI1, SOX2, and OCT4-3’UTR were repressed in

K190R cells at a significant level compared to DDX17-WT (Fig. 4E). In line with this finding, knockdown of HectH9 in DDX17-WT further suppressed the luciferase activities of BMI1, SOX2, and OCT4-3’UTRs compared to DDX17-WT alone (Supplementary Fig. S5J), implicating that ubiquitination of DDX17 may affect BMI1, SOX2, and OCT4 expressions via miRNA biogenesis.

To corroborate this notion, mutations affecting miRNA binding sites in representative stemness genes, including miR-34a and miR-128 for BMI1-3’UTR (41,42) and miR-203b (43) for

OCT4-3’UTR, elevated the luciferase activities of BMI1- and OCT4-3’UTR reporter constructs in both DDX17-WT and DDX17-K190R cells (Supplementary Fig. S5K), demonstrating that stemness genes containing the mutated miRNA binding sites could abolish the effects of

DDX17-processed miRNAs on these genes.

Together, these data demonstrate that DDX17 ubiquitination could regulate microRNAs biogenesis and processing. Since DDX17 expression does not change under hypoxia, we infer that hypoxia-induced DDX17 ubiquitination may affect the interaction of DDX17 with the

Microprocessor.

Ubiquitinated DDX17 Associates with a Ubiquitin-binding protein, p300, and YAP and

Dissociates from the Drosha-DGCR8 complex

Sequestration of DDX17 from Microprocessor by YAP in the Hippo pathway in a cell-density-dependent manner and increased nuclear presence of YAP under hypoxia have been reported (9,44). To observe the localization of YAP under hypoxia, we conducted nuclear/cytosol fractionation and immunoblotting with anti-phospho-S127 antibodies (44). Hypoxia induced YAP translocation into the nucleus via decreasing the levels of phosphorylated YAP (Fig. 5A,

Supplementary Fig. S6, A and B). This results indicated that nuclear YAP may participate in sequestration of DDX17 under hypoxia. Since YAP does not contain an acknowledged ubiquitin binding domain (UBD), we searched for other proteins that might also interact with polyubiquitinated DDX17. We analyzed the interactomes of YAP and DDX17 using the BioGRID database (45) to identify potential ubiquitin-binding protein. Amongst the eight overlapping proteins, we further characterized p300, which contains a ZZ-type zinc finger (ZZ) domain as a possible UBD (46) (Fig. 5B). Multiple sequence alignments showed that the ZZ domain is

conserved among different species (Supplementary Fig. S6C). The co-immunoprecipitation assay showed that under hypoxia DDX17 increased the association with YAP and p300 but decreased interaction with Drosha and DGCR8 (Fig. 5C and Supplementary Fig. S6D). Enhanced interaction under hypoxia between endogenous p300, YAP, and DDX17 was also detected using anti-YAP or anti-p300 antibodies, respectively (Supplementary Fig. S6, E and F).

Overexpression of myc-DDX17 and Flag-p300 followed by immunoprecipitation by anti-myc antibodies revealed that DDX17 and p300 could interact with each other and a stronger association was observed under hypoxia (Supplementary Fig. S6G). To examine whether the ZZ-type zinc finger motif (ZZ) of p300 is a ubiquitin binding motif for the K63 polyubiquitin chains, we co-expressed the CH3 domain of p300 containing the ZZ-type zinc finger motif (a.a. 1643-1857, p300-CH3) or the nuclear co-activator binding domain (a.a. 1858-2153, p300-NCBD) with

DDX17 (Fig. 5D, top). The co-immunoprecipitation result showed that only the ZZ motif interacted with DDX17 (Supplementary Fig. S6H) and this interaction was enhanced under hypoxia (Fig. 5D).

To further investigate whether the ZZ motif of p300 is a UBD, we made two constructs, in which the ZZ motif was deleted (ZZ), and another where the ZZ motif was replaced by the zinc-finger domain of TAB2 (ZZ-TAB2) (Supplementary Fig. S6C) (46). The wild-type CH3 domain and the TAB2-replaced CH3 domain mutant (ZZ-TAB2) had an increased interaction with DDX17 under hypoxia, but not the ZZ-domain deleted mutant (ZZ) (Fig. 5E). Taken

together, we found that p300 is a novel ubiquitin-binding protein that interacts with

K63-polyubiqiutinated DDX17 to enhance the complex formation of DDX17, YAP, and p300 under hypoxia.

Furthermore, to assess whether ubiquitination of DDX17 would affect its interaction with p300, we co-expressed DDX17-WT, polyubiquitinated DDX17 (Ub-DDX17-WT), or

DDX17-K190R with p300 and we found that Ub-DDX17-WT enhanced interaction with p300 whereas DDX17-K190R reduced it, as compared to DDX17-WT (Fig. 5F). The GST pull-down assay showed that Flag-DDX17-WT with K63-polyubiquitinated chain interacted with

GST-p300-CH3-WT, but not GST-p300-CH3-ΔZZ proteins (lane 5 and lane 8, Supplementary

Fig. S6I). DDX17-K190R, on the other hand, could not interact with either GST-p300-CH3-WT or

GST-p300-CH3-ΔZZ (lane 6 and lane 9, Supplementary Fig. S6I), demonstrating a direct ZZ domain contact with DDX17 specifically through the K190-conjugated K63-polyubiquitinated chain. Finally, the co-immunoprecipitation assay showed that under hypoxia, DDX17-WT had an increased association with YAP and p300 and a weakened binding with Drosha and DGCR8 (Fig.

5G). On the contrary, DDX17-K190R was retained at the Microprocessor under hypoxia. Overall, these data suggest that under hypoxia, ubiquitinated DDX17 enhanced the complex formation of

DDX17, YAP, and p300, and decreased its interaction with the Microprocessor, which may lead to decreased biogenesis of anti-stemness miRNAs.

p300 and DDX17 Increase H3K56Ac Occupancy on the Promoters of Stemness-Related

Genes under Hypoxia

p300 is a histone acetyltransferase, and hypoxia-induced association between the ubiquitin receptor, p300, DDX17, and YAP leads to the possibility that the complex could together mediate epigenetic marks of histones and hence regulate the stem-like property in tumors. To search for possible correlation, we screened the histone marks in HectH9-knockdown and

DDX17-knockdown cells and found that only H3K56 acetylation (H3K56Ac) was concurrently decreased (Supplementary Fig. S7A). This result is in line with the previous evidence that

H3K56Ac is implicated in the transcriptional network of pluripotency (47). To look for H3K56Ac binding regions, we downloaded and re-analyzed the H3K56Ac ChIP-seq data from the Roadmap

Epigenomics Project (www.roadmapepigenomics.org/) (48) (the detailed information is described in Method). We found that H3K56Ac in human embryonic stem cell (hESC) H1 lines was deposited at the regions around transcription start site (TSS) of OCT4, SOX2, NANOG, NOTCH1, and BMI1 (Fig. 6A), implicating that H3K56Ac participates in the regulation of these stemness-related genes.

To verify whether HectH9-mediated ubiquitination of DDX17 modulates the level of

H3K56Ac, we knocked down HectH9 in DDX17-overexpressing clones and found that overexpression of DDX17 increased the H3K56Ac levels whereas further suppression of HectH9 reduced their levels (Fig. 6B). Accordingly, upregulation of H3K56Ac was detected when p300

was expressed and further enhanced with the presence of DDX17-WT, but the enhanced level of

H3K56Ac was abrogated by K190R mutant (Fig. 6C). We next determined whether

DDX17/p300/YAP and H3K56Ac will co-occupy at the regions around the TSS of the stemness-related genes. Our qChIP data indicated that under hypoxia, the bindings of DDX17, p300, YAP, and H3K56Ac were elevated at the promoters of BMI1 (at P2), OCT4 (at P3), and

SOX2 (at P4) (Fig. 6D and Supplementary Fig. S7B). These data suggest that hypoxia-induced ubiquitination of DDX17 may promote the localization of p300 onto their target promoters to incur

H3K56Ac. On the contrary, HectH9 suppression resulted in a decrease in the bindings of DDX17, p300, YAP, and H3K56Ac at the promoters of BMI1 (at P2), OCT4 (at P3), and SOX2 (at P4)

(Supplementary Fig. S7C). Accordingly, the qChIP analysis with anti-Flag antibodies in

Flag-DDX17-WT or Flag-DDX17-K190R cells showed that DDX17-WT had a significantly better binding affinity than DDX17-K190R on these promoters (P2 of BMI1, P3 of OCT4, and P4 of

SOX2) (Fig. 6E). Although the H3K56Ac level was perturbed by HectH9 or DDX17 suppression

(Supplementary Fig. S7A), the non-promoter regions of BMI1, SOX2, and OCT4 rich with

H3K56Ac were not affected by HectH9 suppression (Supplementary Fig. S7D), implicating that

H3K56Ac may mainly modulate the promoter regions of its target genes.

Since our previously-identified deubiquitinase, HAUSP, also a substrate of HectH9, mediates the H3K56Ac levels on the EMT genes, e.g. TWIST1, and associates with the super elongation complex (SEC) (24), we examined whether DDX17 plays a role in TWIST1 regulation. The qChIP

data shows that under hypoxia, the binding of DDX17 on the TWIST1 promoter did not change

(Supplementary Fig. S7E). Co-immunoprecipitation analyses show that DDX17 did not interact with HAUSP and HIF-1α, nor did DDX17 show altered binding with the components of the SEC

(AFF4, RNA PolII) (Supplementary Fig. S7, F and G). Moreover, HAUSP occupancy was not changed on the promoter regions of BMI1, OCT4, and SOX2 by hypoxia (Supplementary Fig.

S7B). These data suggest that DDX17 and HAUSP may form different complexes to mediate their own individual downstream genes.

Last, to demonstrate a direct transcriptional effect of DDX17 polyubiquitination on targeted genes, we cloned the parts of OCT4-, SOX2-, and BMI1-promoters encompassing DDX17 binding regions and checked their luciferase activities. Our data show that DDX17-WT had elevated luciferase activities whereas DDX17-K190R did not (Supplementary Fig. S7H), suggesting that non-ubiquitinated DDX17-K190R possesses an attenuated transcriptional co-activation ability, which might be due to K190R’s reduced binding to p300 (Fig 5F) and the reduced level of

H3K56Ac. Accordingly, the nascent RNA levels of the stemness-related genes were down-regulated by HectH9 suppression (Supplementary Fig. S7I). Together, these results suggest that ubiquitinated DDX17/p300/YAP elevates H3K56Ac levels on the promoters of stemness-related genes under hypoxia and transcriptionally upregulates the expression of stemness-related genes.

Co-expression of HectH9 and Six Stemness-related Genes is a Prognostic Factor for HNSCC or Lung Adenocarcinoma Patients

Finally, we tested whether the HectH9-DDX17-six stemness-related genes pathway provides prognostic implications at the clinical level. Since DDX17 protein stability was not subject to

HectH9-mediated ubiquitination, we analyzed the survival difference of patient groups bearing

HectH9 and/or stemness marker expression. HectH9 alone or six stemness-related genes alone did not predict the clinical survival outcome in either HNSCC or lung adenocarcinoma patients

(Supplementary Fig. S7, J and K), suggesting that either HectH9 or six stemness-related genes alone may not be a useful prognostic factor in these two tumor types. However, when the HectH9 and six stemness-related gene signature was highly co-expressed in the patients having these two tumor types, the doubled high expression served as a significant prognostic marker to predict shorter disease-free or overall survival than those whose cancers did not co-express HectH9 and the six stemness markers (Fig. 6F and Supplementary Fig. S7L). Collectively, these data suggest that co-expression of HectH9 and six stemness-related genes could serve as a prognostic marker in predicting the survival of HNSCC or lung adenocarcinoma patients.

In sum, we report that hypoxia-mediated ubiquitination of DDX17 concerts machineries at the post-transcriptional and transcriptional levels, thereby controlling tumor stem-like property and tumor-initiating capability (a model is presented; Fig. 6G).

Discussion

Our findings provide a detailed understanding of the mechanisms by which hypoxia-induced

PTM serves as a central mechanism to control cancer stem-like features via miRNA biogenesis downregulation and histone modification. To our best knowledge, this report is the first to demonstrate that a single PTM can negatively and positively modulate two downstream machineries, where both events synergistically increase cancer stem-like properties.

We demonstrate that K63-polyubiquitinated DDX17 interacts with different protein partners to dictate either miRNA biogenesis or histone modification mechanisms. The fate of association and dissociation of polyubiquitinated DDX17 is determined in part by the presence of nuclear and cytoplasmic YAP. As hypoxia decreases phosphorylated YAP through the SIAH2-LATS signaling pathway (44), nuclear presence of YAP increases (Fig. 5A; Supplementary Fig. S6, A and B).

Nuclear YAP likely sequesters DDX17, causing DDX17 to dissociate from the Drosha-DGCR8 complex, thereby inhibiting miRNA biogenesis. Furthermore, polyubiquitination of DDX17 enhances interaction with p300, a ubiquitin binding protein, to form the YAP–DDX17–p300 complex, which corresponds to increased H3K56 acetylation of stemness-related genes under hypoxia (Fig. 5C and 6D). This concerted regulation of both miRNA biogenesis and histone modification constitutes a unifying mechanism for promoting cancer stem-like features via simultaneously inhibiting the processing of anti-stemness miRNAs and increasing the transcription

of stemness-related genes.

Although global downregulation of miRNAs through impaired miRNA biogenesis has been observed in cancer (49), the functional significance of these miRNAs and their relatedness to cancer stemness features are not well characterized. Mori et al. have reported 317 miRNAs dysregulated by DDX17 knockdown in HaCaT cells and a sequence motif ([GTA]CATC[CTA]) in the 3’ flanking segment of pri-miRNAs recognized by DDX17 (9). Amongst the 11 miRNAs we selected that interact with stemness-related genes, 10 of them exhibit this motif, indicating DDX17 may preferentially recognize and bind to a subset of miRNAs. Polyubiquitination of DDX17 at a.a.

190 located between motif-1 and motif-2 inside domain I, which binds to nucleic acids (4), may also disrupt the binding of DDX17 to these miRNAs. Thus, our findings suggest that disruption of

DDX17 binding likely represses miRNA biogenesis of anti-stemness miRNAs.

DDX17 is a homolog of DDX5, which also plays a role in miRNA processing (50). However,

DDX5 does not have a Lys residue corresponding to K190 on DDX17 that could be ubiquitinated by HectH9. In addition, DDX5 does not interact with YAP (9). Therefore, the signaling pathway regulating DDX5-mediated miRNA processing should be different from the HectH9-DDX17 pathway. A report has shown that p300-mediated acetylation of DDX17, but not of DDX5, contributes to tumorigenesis (51), supporting our observation of the complex formation between p300 and DDX17.

Ubiquitin-binding proteins are responsible for decoding ubiquitinated target signals into

biochemical cascades (52). K63 conjugates have long been associated with DNA repair and the innate immunity in which the ubiquitin system is responsible for recruitment of DNA repair factors and for the activation of NF-κB (53). In this study, we demonstrate that p300 is a novel ubiquitin-binding protein that can recognize K63-ubiquitinated DDX17. Complexing between p300 and DDX17 promotes transcription of stemness-related genes by H3K56Ac and provides the pro-growth signals in tumorigenesis. These results are consistent with the role of H3K56Ac in controlling core transcriptional network to regulate pluripotency (47). Surprisingly, HectH9 suppression seems to slightly reduce the HIF-1α protein level without the common degradation domain, ODD, (Fig. 1F), implicating that HectH9 may reversely regulate HIF-1α via other pathways independent of HIF-1α degradation to sustain such a positive feedback.

We further show that deposition of H3K56Ac can be found on the promoters of stemness-related genes, particularly BMI1, SOX2, OCT4, along with ubiquitinated DDX17.

Hypoxia-induced co-occupancy of DDX17–p300–YAP and H3K56Ac at the BMI1-, SOX2-, and

OCT4-promoters implies that DDX17 ubiquitination may promote the localization of p300 to the promoters of stemness-related genes and upregulate H3K56 acetylation. However, we could not find any consensus site related to HIF-1α or YAP/TEAD binding sites at the regions where DDX17 binds to (p2 regions in Bmi1, p3 in Oct4, and p4 in Sox2) using the pair-wise alignment. Therefore, we exclude a putative role for YAP in sequence specific targeting of the p300/YAP/DDX17 complex. So far, it has not been clearly demonstrated what signaling facilitates H3K56Ac

deposition on the promoters of these stemness-related genes, but our results suggest it could be hypoxia. Since embryonic cells also reside in a hypoxic environment (54), our findings suggest that the HectH9-DDX17 axis could act as a driving force to maintain stem-cell pluripotency in embryos as well. Our results demonstrate that the HectH9-DDX17 axis could determine cancer stem-like features and that the HectH9-six stemness-related gene (OCT4, SOX2, NANOG, BMI1,

NOTCH1, NOTCH2) signature predicts the survival outcome of patients with HNSCC and lung adenocarcinomas.

Our results elucidate the mechanism delineating the connection between global downregulation of miRNAs and tumorigenesis. The concerted regulation of miRNA biogenesis and histone modification by polyubiquitinated DDX17 highlights the importance of blocking

DDX17 ubiquitination as a promising therapeutic strategy in cancer treatment. Altogether, we introduce a unifying model through post-translational modification that defines cancer stem-like features. These results will be important both mechanistically and therapeutically for cancer patient management.

Acknowledgements

We thank Dr. Wei Gu for providing the HectH9 plasmids and Dr. M.-H. Yang for providing a primary culture of head-and-neck squamous cells. We thank the RNAi Core Facility at Academia

Sinica, Taiwan for technical support.

Funding

This work was supported by the “Drug Development Center, China Medical University" from

The Featured Areas Research Center Program within the framework of the Higher Education

Sprout Project by the Ministry of Education (MOE) in Taiwan. This work was also supported in part to K.J.W. by Ministry of Science and Technology Summit grant (MOST 107–

2745-B-039-001, MOST 106–2745-B-039-001), center of excellence for cancer research at

Taipei Veterans General Hospital (MOHW105-TDU-B-211-134-003); and to S.H.K by Ministry of

Science and Technology (MOST 105-2320-B-039-063) and China Medical University (CMU

103-RAP-03).

References:

1. Pattabiraman DR, Weinberg RA. Tackling the cancer stem cells - what challenges do they pose? Nat Rev Drug Discov. 2014;13:497–512.

2. Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN, et al. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell. 2018;173:338-354.e15.

3. Guallar D, Wang J. RNA-binding proteins in pluripotency, differentiation, and reprogramming. Front Biol. 2014;9:389–409.

4. Linder P, Jankowsky E. From unwinding to clamping - the DEAD box RNA helicase family. Nat Rev Mol Cell Biol. 2011;12:505–16.

5. Dardenne E, Polay Espinoza M, Fattet L, Germann S, Lambert M-P, Neil H, et al. RNA helicases DDX5 and DDX17 dynamically orchestrate transcription, miRNA, and splicing programs in cell differentiation. Cell Rep. 2014;7:1900–13.

6. Ha M, Kim VN. Regulation of microRNA biogenesis. Nat Rev Mol Cell Biol. 2014;15:509–24.

7. Guil S, Cáceres JF. The multifunctional RNA-binding protein hnRNP A1 is required for processing of miR-18a. Nat Struct Mol Biol. 2007;14:591–6.

8. Kawai S, Amano A. BRCA1 regulates microRNA biogenesis via the DROSHA microprocessor complex. J Cell Biol. 2012;197:201–8.

9. Mori M, Triboulet R, Mohseni M, Schlegelmilch K, Shrestha K, Camargo FD, et al. Hippo signaling regulates microprocessor and links cell-density-dependent miRNA biogenesis to cancer. Cell. 2014;156:893–906.

10. Suzuki HI, Yamagata K, Sugimoto K, Iwamoto T, Kato S, Miyazono K. Modulation of microRNA processing by p53. Nature. 2009;460:529–33.

11. Ceppi P, Peter ME. MicroRNAs regulate both epithelial-to-mesenchymal transition and cancer stem cells. Oncogene. 2014;33:269–78.

12. Germann S, Gratadou L, Zonta E, Dardenne E, Gaudineau B, Fougère M, et al. Dual role of the ddx5/ddx17 RNA helicases in the control of the pro-migratory NFAT5 transcription factor. Oncogene. 2012;31:4536–49.

13. Samaan S, Tranchevent L-C, Dardenne E, Polay Espinoza M, Zonta E, Germann S, et al. The Ddx5 and

Ddx17 RNA helicases are cornerstones in the complex regulatory array of steroid hormone-signaling pathways. Nucleic Acids Res. 2014;42:2197–207.

14. Chen ZJ, Sun LJ. Nonproteolytic functions of ubiquitin in cell signaling. Mol Cell. 2009;33:275–86.

15. Kim HC, Huibregtse JM. Polyubiquitination by HECT E3s and the Determinants of Chain Type Specificity. Mol Cell Biol. 2009;29:3307–18.

16. King B, Boccalatte F, Moran-Crusio K, Wolf E, Wang J, Kayembe C, et al. The ubiquitin ligase Huwe1 regulates the maintenance and lymphoid commitment of hematopoietic stem cells. Nat Immunol. 2016;17:1312–21.

17. Confalonieri S, Quarto M, Goisis G, Nuciforo P, Donzelli M, Jodice G, et al. Alterations of ubiquitin ligases in human cancer and their association with the natural history of the tumor. Oncogene. 2009;28:2959–68.

18. Peter S, Bultinck J, Myant K, Jaenicke LA, Walz S, Müller J, et al. Tumor cell‐specific inhibition of MYC function using small molecule inhibitors of the HUWE1 ubiquitin ligase. EMBO Mol Med. 2014;6:1525–41.

19. Plaks V, Kong N, Werb Z. The cancer stem cell niche: how essential is the niche in regulating stemness of tumor cells? Cell Stem Cell. 2015;16:225–38.

20. Mathieu J, Zhang Z, Zhou W, Wang AJ, Heddleston JM, Pinna CMA, et al. HIF induces human embryonic stem cell markers in cancer cells. Cancer Res. 2011;71:4640–52.

21. Gregory RI, Yan K-P, Amuthan G, Chendrimada T, Doratotaj B, Cooch N, et al. The Microprocessor complex mediates the genesis of microRNAs. Nature. 2004;432:235–40.

22. Yang M-H, Wu M-Z, Chiou S-H, Chen P-M, Chang S-Y, Liu C-J, et al. Direct regulation of TWIST by HIF-1alpha promotes metastasis. Nat Cell Biol. 2008;10:295–305.

23. Liao T-T, Hsu W-H, Ho C-H, Hwang W-L, Lan H-Y, Lo T, et al. let-7 Modulates Chromatin Configuration and Target Gene Repression through Regulation of the ARID3B Complex. Cell Rep. 2016;14:520–33.

24. Wu H-T, Kuo Y-C, Hung J-J, Huang C-H, Chen W-Y, Chou T-Y, et al. K63-polyubiquitinated HAUSP deubiquitinates HIF-1α and dictates H3K56 acetylation promoting hypoxia-induced tumour progression. Nat Commun [Internet]. 2016 [cited 2017 May 22];7. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5155157/

25. Moffat J, Grueneberg DA, Yang X, Kim SY, Kloepfer AM, Hinkle G, et al. A lentiviral RNAi library

for human and mouse genes applied to an arrayed viral high-content screen. Cell. 2006;124:1283–98.

26. Chung I-F, Chang S-J, Chen C-Y, Liu S-H, Li C-Y, Chan C-H, et al. YM500v3: a database for small RNA sequencing in human cancer research. Nucleic Acids Res. 2017;45:D925–31.

27. Chou C-H, Shrestha S, Yang C-D, Chang N-W, Lin Y-L, Liao K-W, et al. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 2018;46:D296–302.

28. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.

29. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9:R137.

30. Zhu LJ, Gazin C, Lawson ND, Pagès H, Lin SM, Lapointe DS, et al. ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics. 2010;11:237.

31. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013;41:D991-995.

32. Li Q, Birkbak NJ, Gyorffy B, Szallasi Z, Eklund AC. Jetset: selecting the optimal microarray probe set to represent a gene. BMC Bioinformatics. 2011;12:474.

33. Terry M. Therneau. A Package for Survival Analysis in S. version 2.38. 2015; Available from: https://CRAN.R-project.org/package=survival

34. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [Internet]. 2017. Available from: https://www.R-project.org/.

35. Ginestier C, Hur MH, Charafe-Jauffret E, Monville F, Dutcher J, Brown M, et al. ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome. Cell Stem Cell. 2007;1:555–67.

36. Hjerpe R, Aillet F, Lopitz-Otsoa F, Lang V, England P, Rodriguez MS. Efficient protection and isolation of ubiquitylated proteins using tandem ubiquitin-binding entities. EMBO Rep. 2009;10:1250–8.

37. Zhong Q, Gao W, Du F, Wang X. Mule/ARF-BP1, a BH3-only E3 ubiquitin ligase, catalyzes the polyubiquitination of Mcl-1 and regulates apoptosis. Cell. 2005;121:1085–95.

38. Yang M-H, Hsu DS-S, Wang H-W, Wang H-J, Lan H-Y, Yang W-H, et al. Bmi1 is essential in Twist1-induced epithelial-mesenchymal transition. Nat Cell Biol. 2010;12:982–92.

39. Dean M, Fojo T, Bates S. Tumour stem cells and drug resistance. Nat Rev Cancer. 2005;5:275–84.

40. Iliou MS, da Silva-Diz V, Carmona FJ, Ramalho-Carvalho J, Heyn H, Villanueva A, et al. Impaired DICER1 function promotes stemness and metastasis in colon cancer. Oncogene. 2014;33:4003–15.

41. Pillai MM, Gillen AE, Yamamoto TM, Kline E, Brown J, Flory K, et al. HITS-CLIP reveals key regulators of nuclear receptor signaling in breast cancer. Breast Cancer Res Treat. 2014;146:85–97.

42. Kishore S, Jaskiewicz L, Burger L, Hausser J, Khorshid M, Zavolan M. A quantitative analysis of CLIP methods for identifying binding sites of RNA-binding proteins. Nat Methods. 2011;8:559–64.

43. Chi SW, Zang JB, Mele A, Darnell RB. Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature. 2009;460:479–86.

44. Ma B, Chen Y, Chen L, Cheng H, Mu C, Li J, et al. Hypoxia regulates Hippo signalling through the SIAH2 ubiquitin E3 ligase. Nat Cell Biol. 2015;17:95–103.

45. Chatr-aryamontri A, Oughtred R, Boucher L, Rust J, Chang C, Kolas NK, et al. The BioGRID interaction database: 2017 update. Nucleic Acids Res. 2017;45:D369–79.

46. Rahighi S, Dikic I. Selectivity of the ubiquitin-binding modules. FEBS Lett. 2012;586:2705–10.

47. Xie W, Song C, Young NL, Sperling AS, Xu F, Sridharan R, et al. Histone h3 lysine 56 acetylation is linked to the core transcriptional network in human embryonic stem cells. Mol Cell. 2009;33:417–27.

48. Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A, et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat Biotechnol. 2010;28:1045–8.

49. Kumar MS, Lu J, Mercer KL, Golub TR, Jacks T. Impaired microRNA processing enhances cellular transformation and tumorigenesis. Nat Genet. 2007;39:673–7.

50. Newman MA, Hammond SM. Emerging paradigms of regulated microRNA processing. Genes Dev. 2010;24:1086–92.

51. Mooney SM, Goel A, D’Assoro AB, Salisbury JL, Janknecht R. Pleiotropic effects of p300-mediated acetylation on p68 and p72 RNA helicase. J Biol Chem. 2010;285:30443–52.

52. Husnjak K, Dikic I. Ubiquitin-binding proteins: decoders of ubiquitin-mediated cellular functions. Annu Rev Biochem. 2012;81:291–322.

53. McCool KW, Miyamoto S. DNA damage-dependent NF-κB activation: NEMO turns nuclear signaling inside out. Immunol Rev. 2012;246:311–26.

54. Moreno M, Fernández V, Monllau JM, Borrell V, Lerin C, de la Iglesia N. Transcriptional Profiling of Hypoxic Neural Stem Cells Identifies Calcineurin-NFATc4 Signaling as a Major Regulator of Neural Stem Cell Biology. Stem Cell Rep. 2015;5:157–65.

Figure Legends:

Figure 1. Hypoxia/HIF-1α-upregulated HectH9 plays a role in tumor stem-like properties.

(A) qRT-PCR analysis (left) for endogenous HectH9 mRNA levels and Western blot analysis (right) for HectH9 protein levels under normoxia (N) or hypoxia (H) in FaDu, OECM-1, H1299, and

MCF7 cells. VEGF serves as the positive control for hypoxia for qRT-PCR. Data are represented as the mean ± s.e.m. for biological triplicate experiments. (B) Western blot analysis to test HectH9 knockdown in OECM-1 cells with either control or two HectH9 shRNAs. (C) Left: Representative pictures of tumor sphere formation of control and HectH9-knockdown clones of OECM-1 cells.

Right: Representative pictures of colony formation of control and HectH9-knockdown clones of

OECM-1 cells. Scale bar: 100 m. (D) Left: Number of tumor sphere-formation activity of control vs. HectH9-knockdown clones of OECM-1 cells measured by primary, secondary, and tertiary spheres. Right: Number of colony formation of control vs. HectH9-knockdown clones of OECM-1 cells. Data are represented as the mean ± s.e.m. for biological triplicate experiments. (E) The population of ALDH1+ cells analyzed by flow cytometry in control vs. HectH9-knockdown clones of OECM-1 cells. (F) Western blot analysis to test HIF-1α(ΔODD) overexpression and HectH9 knockdown in FaDu cells. (G) Representative pictures of tumor sphere formation of the indicated stable clones of FaDu cells. (H) Number of tumor sphere-formation activity of the indicated stable clones of FaDu cells measured by primary, secondary, and tertiary spheres. Scale bar: 100 m.

Data are represented as the mean ± s.e.m. for biological triplicate experiments. (I) Representative

pictures of s.c. implantation at 103 of the indicated stable clones in FaDu cells. n = 6/each group.

Scale bar: 10 mm. (J) Average tumor weight of the in vivo tumor-initiating assay at 103 cell implantation (n = 6 mice/group). The control weight was arbitrarily set as 1. The asterisks (*) indicated statistical significance (p < 0.05) between experimental and control clones, t-test. # indicated there was no tumor formation.

Figure 2. DDX17 is poly-ubiquitinated by HectH9 via K63-linkage and K190R reduces tumor stem-like properties

(A) Schematic representation of the experimental procedure of immunoprecipitation using

Flag-tagged K63-specific tandem ubiquitin-binding entity (K63-specific TUBEs). (B) Pie chart showing GO annotation for K63-ubiquitinated targets using DAVID functional annotation tool. (C)

Western blot analysis of the interaction between endogenous HectH9 and DDX17 by co-IP using

H1299 lysates with either anti-HectH9 or anti-DDX17 antibodies. IgG was used as a negative control. (D) Expressions of K63-, K48-linked, or total ubiquitination of DDX17 in control or

HectH9-knockdown cells under normoxia or hypoxia treatment. Lysates were immunoprecipitated with DDX17 using anti-DDX17 antibodies, followed by immunoblotting using the indicated antibodies. (E) Detection of HA-ubiquitin of DDX17-WT (WT) or DDX17-K190R (K190R) in

HA-K48R-Ub (left) or HA-K63R-Ub (right) transfectants. 293T cells co-transfected with the indicated plasmids were subjected to normoxia (N) or hypoxia (H) treatment followed by Western

blot with the indicated antibodies. 293T cells co-transfected with the indicated plasmids were subjected to normoxia (N) or hypoxia (H) treatment followed by Western blot analysis with the indicated antibodies. (F) Representative pictures of tumor sphere formation of WT or K190R clones in H1299 and FaDu cells. Scale bar: 100 m. (G) Left: Number of tumor sphere-formation activity of WT or K190R clones measured by primary, secondary, and tertiary spheres. Right:

Number of colony formation of WT or K190R clones. Data are represented as the mean ± s.e.m. for biological triplicate experiments. The asterisk (*) indicated statistical significance (p < 0.05) between experimental and control clones, t-test.

Figure 3. DDX17-K190R reduces stemness-related gene expressions and protein levels as well as tumour-initiating capability

(A) qRT-PCR analysis of stemness-related gene expressions in HectH9- (top) or

DDX17-knockdown (bottom) H1299 cells. Data are represented as the mean ± s.e.m. for biological triplicate experiments. (B) Western blot analysis to detect the protein levels of stemness-related genes under normoxia (N) or hypoxia (H) in H1299 cells. (C) qRT-PCR analysis of stemness-related gene expressions in H1299/WT and H1299/K190R clones. Gfi1 as a negative control. Data are represented as the mean ± s.e.m. for biological triplicate experiments. *p < 0.05 between DDX17-WT and DDX17-K190R. t-test. (D) Western blot analysis of stemness-related gene expressions in H1299/WT and H1299/K190R clones. (E) qRT-PCR analysis of stemness-related gene expressions of FaDu/WT and FaDu/K190R stable clones. Gfi1 as a negative 46

control. Data are represented as the mean ± s.e.m. for biological triplicate experiments. *p < 0.05 between DDX17-WT and DDX17-K190R. t-test. (F) Western blot analysis of stemness-related gene expressions of FaDu/WT and FaDu/K190R stable clones. (G-H) The population of ALDH1+ cells analyzed by flow cytometry in WT and K190R clones in H1299 (G) and FaDu cells (H). (I)

Representative pictures of s.c. implantation at 103 of FaDu/WT and FaDu/K190R cells. n=6/each group. Scale bar: 5 mm. (J) Average tumor weight of the in vivo tumor-initiating assay at 104 and

103 cell implantation of DDX17-WT- or DDX17-K190R-overexpression clones in FaDu cells (n=6 mice/group). The control weight was arbitrarily set as 1. *p < 0.05 between DDX17-WT and

DDX17-K190R. t-test. (K) Tumor-killing effect of cisplatin using the MTS assay. Data are represented as the mean ± s.e.m. for biological triplicate experiments. *p < 0.05. t-test. (L) The apoptosis ratio of the stable clones in (K) with or without cisplatin treatment, as determined by annexin-V and PI staining using flow cytometry. Data are represented as the mean ± s.e.m. for biological triplicate experiments. *p < 0.05. t-test.

Figure 4. Non-ubiquitinated DDX17 promotes the miRNA biogenesis of a subset of anti-stemness miRNAs

(A) The miRNA-gene network showing miRNAs targeting to the stemness-related genes based on experimentally-validated evidence (solid line) and predicted algorithms (dashed line). The thickness of the lines indicates the power of association, which is based on the number of evidence or algorithms. (B) Left: qRT-PCR analysis of mature miRNA levels normalized to RNU-48 in 47

H1299 cells after cells were treated with normoxia or hypoxia. Right: Relative pri-miRNA expression measured by qRT-PCR normalized to 18S in the same cells. Inset: Glut-1 expression as the positive control of the experiment. Data are represented as the mean ± s.e.m. for biological triplicate experiments. (C) Left: qRT-PCR analysis of mature miRNA levels normalized to

RNU-48 in HectH9-overexpressing H1299 cells. Right: Relative pri-miRNA expression measured by qRT-PCR normalized to 18S in the same cells. Inset: HectH9 expression as the positive control of the experiment. Data are represented as the mean ± s.e.m. for biological triplicate experiments.

(D) Left: qRT-PCR analysis of mature miRNA levels normalized to RNU-48 of DDX17-WT or

DDX17-K190R-expressing H1299 cells. Right: Relative pri-miRNA expression measured by qRT-PCR normalized to 18S of the same cells. Data are represented as the mean ± s.e.m. for biological triplicate experiments. (E) Luciferase assays with a BMI1-, SOX2-, or OCT4-3’UTR reporter in DDX17-WT and DDX17-K190R-expressing cells. Luciferase activity was normalized to that of pCMV--gal. Data are represented as the mean ± s.e.m. for biological triplicate experiments. The asterisk (*) indicated statistical significance (p < 0.05) between experimental and control clones, t-test.

Figure 5. Increased association of ubiquitinated DDX17 with p300 (the ubiquitin-binding protein), and YAP and reduced association with the Microprocessor

(A) Top: Western blot analyses of YAP expression after nuclear and cytosol fractionation in FaDu

(left) cells. GAPDH and H3 serve as the cytosol and nucleus control, respectively. Bottom: 48

Western blot analysis of protein of interests under normoxia (N) and (H) in FaDu cells. (B)

Interactome analysis of YAP and DDX17 using BioGRID. (C) Detection of the interaction between endogenous YAP, p300, Drosha, DGCR8, and DDX17 by co-IP using normoxic (N) or hypoxic (H) FaDu lysates with anti-DDX17 antibodies. IgG was used as a negative control. (D)

Top: Schematic diagram of the domains of the p300 protein. Bottom: Detection of the interaction between endogenous DDX17 and truncated p300-CH3 WT or p300-NCBD by immunoprecipitation of DDX17 with anti-DDX17 antibodies. IgG was used as a negative control.

(E) Top: Schematic diagram of the constructs. Bottom: Detection of the interaction between endogenous DDX17 and truncated p300-CH3 WT or p300-CH3ZZ, p300-CH3ZZ-TAB2 under normoxia (N) or hypoxia (H) by immunoprecipitation of DDX17 with anti-DDX17 antibodies. (F)

Detection of the interaction between Flag-p300 and myc-tagged DDX17-WT, Ub-DDX17-WT, or

DDX17-K190R by co-IP with anti-myc antibodies in 293T cells, followed by immunoblotting with the indicated antibodies. For enhancement of K63-polyubiquitinated DDX17 (Ub-DDX17-WT),

293T cells were co-transfected with pMyc-DDX17-WT and pHA-ubiquitin-K63 plasmids.

Ubiquitinated DDX17 was detected by anti-ubiquitin-K63 antibodies followed by immunoprecipitation of myc-DDX17-WT or myc-DDX17-K190R. (G) Detection of the interaction between p300, YAP, Drosha, DGCR8, and Flag-tagged DDX17-WT or DDX17-K190R under normoxia (N) or hypoxia (H). Lysates were immunoprecipitated by anti-Flag antibodies, followed by Western blot with the indicated antibodies.

Figure 6. Polyubiquitinated DDX17–p300–YAP is associated with the enhanced H3K56Ac level on the promoters of stemness-related genes and the HectH9-six stemness-related genes signature serves as prognostic marker in HNSCC

(A) Visualization of the H3K56Ac peaks in the BMI1, NANOG, NOTCH1, OCT4, and SOX2 promoters. The y axis is normalized read counts. (B) Western blot analysis of H3K56Ac levels.

DDX17-overexpressing H1299 cells were transduced with HectH9-knockdown or control lentiviruses, followed by immunoblotting with the indicated antibodies. H3K9Ac and H3K14Ac were used as negative controls. (C) Levels of H3K56Ac in control, DDX17-WT-, or

DDX17-K190R-overexpressing H1299 cells transfected with the expression plasmid for p300 or control. (D) qChIP analysis of BMI1, SOX2, and OCT4 promoters of H1299 cells under normoxia

() or hypoxia () by immunoprecipitating DDX17, p300, H3K56Ac, or YAP with their specific antibodies. Data are represented as the mean ± s.e.m. for biological triplicate experiments. *p <

0.05. t-test. (E) qChIP analysis of BMI1, SOX2, and OCT4 promoters of Flag-tagged DDX17-WT or DDX17-K190R-overexpressing H1299 cells. Sonicated lysates were immunoprecipitated with anti-Flag or anti-IgG antibodies. Data are represented as the mean ± s.e.m. for biological triplicate experiments. *p < 0.05 between DDX17-WT and DDX17-K190R. t-test. (F) Kaplan–Meier curve for the two merged datasets of HNSCC cancer cohorts depicting disease-free survival of patients with the signature of HectH9 and six stemness-related genes. HECTH9 and the aggregate expression score of six genes (i.e. OCT4, SOX2, NANOG, BMI1, NOTCH1, and NOTCH2) greater 50

or lower than the median of the entire population is classified as high or low, respectively. N of double high = 17. N of the others =67. P values are based on a log-rank test. (G) A proposed model of hypoxia-induced HectH9-mediated DDX17 polyubiquitination and its role in miRNA biogenesis as well as transcription activation. HIF-1α induces HectH9, whose expression mediates cancer stem-like properties. HectH9 ubiquitinates the downstream target, DDX17, via K63 linkage, which, in turn, affects the functions of DDX17 in terms of miRNA biogenesis and transcription.

Polyubiquitinated DDX17 shows preference binding to the ubiquitin-binding protein, p300, and

YAP as opposed to the Microprocessor. The polyubiquitinated DDX17–p300–YAP complex is associated with the increased level of H3K56 acetylation. Together, hypoxia-induced DDX17 polyubiquitination post-transcriptionally and transcriptionally regulates stem-like properties and tumorigenesis in cancer.

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Figure 3

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Figure 4

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Figure 5 FaDu A B C F IP: myc FaDu YAP-interacting Input IP: IgG DDX17 CY NU proteins N H N H N H N H N H myc-DDX17 - 315 75 IB: p300 Flag-p300 + + + + IB: YAP WWTR1 187 315 YWHAE 75 IB: Flag 35 IB: YAP IB: GAPDH RUNX2 IB: myc 95 180 IB: H3 17 PGLS IB: Drosha Input: 8 p300 315 ELAVL1 130 IB: Flag FaDu NEDD8 IB: DGCR8 173 95 N H PIN1 IB: myc IB: DDX17 75 75 IB: P-YAP IB: β-actin IB: HIF-1α 35 IB: YAP 75 100 DDX17-interacting 180 75 IP: myc-DDX17 IB: DDX17 proteins IB: β-actin 35 130 130 IB: K63-Ub IB: HIF-1α E IB: β-actin 48 G 1643 1857 IP: Flag Flag-p300-CH3 WT ZZ D Flag-DDX17 - WT K190R ZZ-type Flag-p300-CH3 ZZ 1 2414 N H N H N H Flag-p300-CH3 ZZ-TAB2 TAB2 p300 CH1CH1 KIXKIX BD BDHAT CH3 NCBD IB: p300 315 1643-1857 1858-2153 IB: YAP 75 130 IP: IgG DDX17 IP: DDX17 IB: DGCR8 N H N H N H N H Flag-p300-CH3 WT ZZ ZZ-TAB2 180 Flag-p300-CH3 + + - - + + - - N H N H N H IB: Drosha Flag-p300-NCBD - - + + - - + + 35 IB: Flag IB: Flag IB: DDX17 75 28 75 75 42 IB: DDX17 Input: IB: Flag Input: 31 IB: p300315 35 Input: IB: Flag 28 IB: YAP 75 75 IB: DDX17 130 75 100 IB: DDX17 IB: DGCR8 IB: HIF-1α IB: β-actin IB: Drosha 180 53 35

IB: Flag 42 IB: Flag 75

31 IB: β-actin Downloaded from cancerres.aacrjournals.org on September 30, 2021. © 2019 American Association for Cancer 35 Research. Author Manuscript Published OnlineFirst on March 15, 2019; DOI: 10.1158/0008-5472.CAN-18-2376 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Figure 6 B Flag-DDX17 C A 500 bp scri-si HectH9-si H1299 30 0 1 2 1 2 BMI1 17 30 IB: H3K56Ac Flag-DDX17 - - 0 17 30 NANOG IB: H3 Flag-p300 - + + + 0 17 NOTCH1 IB: H3K9Ac 17 IB: H3K56Ac 30

Read counts Read 0 17 IB: H3K14Ac 17 IB: H3 15 OCT4

0 IB: Flag IB: Flag-p300 315 SOX2 75 IB: HectH9 IB: Flag-DDX17 75 D BMI1 OCT4 SOX2 315 IB: β-actin IB: β-actin 35 35

------P1P2 P3 P4 P5 P6 P7 P1P2 P3 P4 P5 P6 P7 P8 P1 P2 P3 P4 P5 P6 P7 E Bmi1 promoter Oct4 promoter Sox2 promoter Flag IgG Flag IgG D D X 1 7 Flag IgG 2.52.5 0.150.15 D D X 1 7 D D X 1 7 3 5 1 5 DDX17 5 DDX17 2 . 0 15 2.0 DDX17 2.0

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 Normoxia  Hypoxia Downloaded from cancerres.aacrjournals.org on September 30, 2021. © 2019 American Association for Cancer Research. Author Manuscript Published OnlineFirst on March 15, 2019; DOI: 10.1158/0008-5472.CAN-18-2376 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Regulation of miRNA biogenesis and histone modification by K63-polyubiquitinated DDX17 controls cancer stem-like features

Shih-Han Kao, Wei-Chung Cheng, Yi-Ting Wang, et al.

Cancer Res Published OnlineFirst March 15, 2019.

Updated version Access the most recent version of this article at: doi:10.1158/0008-5472.CAN-18-2376

Supplementary Access the most recent supplemental material at: Material http://cancerres.aacrjournals.org/content/suppl/2019/03/15/0008-5472.CAN-18-2376.DC1

Author Author manuscripts have been peer reviewed and accepted for publication but have not yet Manuscript been edited.

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