The RNA N6-methyladenosine (m6A) writer methyltransferase-like 3 (METTL3) is upregulated in many types of cancer and promotes cancer progression by increasing expression of several oncogenes. Therefore, a better understanding of the mechanisms regulating METTL3 expression and the key targets of METTL3 in cancer cells could provide new therapeutic targets. In this study, we found that activated JNK signaling is associated with increased METTL3 expression in bladder cancer. Knockdown of JNK1 or administration of a JNK inhibitor impaired the binding of c-Jun with the METTL3 promoter, thereby decreasing the expression of METTL3 and global RNA m6A levels. Moreover, RNA m6A sequencing indicated enrichment of m6A in the 3′-UTR of immune checkpoint PD-L1 mRNA, which could be recognized by the m6A reader IGF2BP1 to mediate RNA stability and expression levels of PD-L1. Inhibition of JNK signaling suppressed m6A abundance in PD-L1 mRNA, leading to decreased PD-L1 expression. Functionally, METTL3 was essential for bladder cancer cells to resist the cytotoxicity of CD8+ T cells by regulating PD-L1 expression. Additionally, JNK signaling contributed to tumor immune escape in a METTL3-dependent manner both in vitro and in vivo. These data reveal the JNK/METTL3 axis as a mechanism of aberrant m6A modification and immune regulation in bladder cancer.

Significance:

The identification of a novel m6A-dependent mechanism underlying immune system evasion by bladder cancer cells reveals JNK signaling as a potential target for bladder cancer immunotherapy.

Bladder cancer is one of the most common cancers, affecting approximately 573,000 people worldwide (1). Bladder cancer is an important public health concern because it has the highest risk of recurrence of all malignancies (2). Eventually most patients die due to treatment failure resulting from drug resistance. In the last decade, there has been rapid progress in cancer immunotherapy, and five checkpoint inhibitors have been approved as first-line (cisplatin ineligible) or second-line therapies for patients with metastatic bladder cancer (3). However, PD-1/PD-L1 blockade monotherapies are efficacious in only about 20% of patients with bladder cancer (4, 5), Therefore, a deeper understanding of the regulatory mechanisms for PD-L1 will undoubtedly benefit patients with bladder cancer by improving the efficacy of current PD-1/PD-L1 blockade treatments.

Transcriptional and epigenetic machinery that respond to external signals contribute to regulation of gene expression and ensure coordinated cellular behaviors and fate decisions. In parallel with the known roles for DNA and protein modifications in gene regulation, recent work has illuminated the regulation of RNA metabolism through processes such as RNA modification and editing as an important posttranscriptional regulatory mechanism (6). Over 100 different RNA modifications have been found to have the potential to alter RNA function. N6-methyladenosine (m6A) is one of the most prevalent internal mRNA modifications in eukaryotic cells and recently emerged as an important regulator of gene expression (7). Numerous lines of evidence have shown that m6A methylation plays critical roles regulating gene expression in development and disease.

In mammalian cells, m6A is added to mRNAs or noncoding RNAs by a methyltransferase, or “writer”, complex consisting of methyltransferase-like 3 (METTL3), METTL14, Wilms' tumor 1 associated protein (WTAP), VIRMA (KIAA1429), and RBM15 (8–11). It is removed by two demethylases, the fat mass- and obesity-associated protein (FTO; ref. 12) and alkylation repair homolog protein 5 (ALKBH5; ref. 13). This illustrates the dynamic nature of m6A modification. The regulatory function of m6A is mediated by m6A binding proteins (also referred to as “readers”), including YTH domain–containing proteins (YTHDC1, YTHDC2, YTHDF1, YTHDF2 and YTHDF3), insulin-like growth factor 2 (IGF2) mRNA-binding proteins (IGF2BP1, IGF2BP2 and IGF2BP3) and other readers such as eIF3D, FMR1, HNRNPC, HNRNPG, and HNRNPA2B1. m6A modification has been implicated in regulating mRNA splicing, nuclear export, mRNA stability, translation (14), as well as microRNA processing (15). It is therefore important in various physiologic and pathologic processes, such as cancer (16) and immune response (17).

PD-L1 is broadly expressed in cancer cells, as well as in multiple types of cells in the tumor microenvironment (TME), including macrophages, dendritic cells, T cells, and fibroblasts (18). The functions and regulatory mechanisms related to PD-L1 in reducing antitumor immunity have been intensively studied. The expression of PD-L1 is elaborately regulated at different levels, including gene transcription, posttranscriptional and posttranslational modifications, and exosomal transport (19). However, epigenetic regulation of PD-L1 expression, especially involving RNA modification, has been rarely studied.

In the current study, we found through m6A sequencing that there is a significant m6A signal around the stop codon in PD-L1 mRNA. This suggests that PD-L1 expression might also be regulated by m6A modification. m6A near the stop codon of PD-L1 mRNA is induced by aberrant expression of METTL3, recognized by IGF2BP1 and contributes to mRNA stability. We further investigated the upstream signaling of METTL3 and found that the JNK pathway sustains METTL3 expression by promoting the binding of c-Jun with the promoter for METTL3. This in turn regulates the mRNA m6A level and expression of PD-L1. Knockdown of JNK1 and treatment with the JNK inhibitor SP600125 both effectively inhibit the expression of PD-L1 and elevate the cytotoxicity of T cells to bladder cancer cells. Our data reveal the regulation mechanism for PD-L1 by mRNA modification and suggest that inhibiting JNK signaling might be a promising strategy for treatment of bladder cancer.

Clinical samples and cell lines

The human samples used to validate JNK activity were collected as described in our previous study (20, 21). Thirty-five pairs of bladder cancer tissues and matched para-tumor tissues (PT) from the same patient were collected with written consent prior to enrollment. The matched PT tissues, which were collected not less than 3 cm from the tumor margin (Supplementary Fig. S1), were recognized to be histologically normal by two independent experts in pathology and used as a noncancerous group in IHC analysis to reduce individual-specific and anatomical site-specific effects (22). The use of clinical samples was evaluated and approved by the Biomedical Ethics Committee of Anhui Medical University (20180346). The experimental methods complied with the Declaration of Helsinki.

Bladder cancer cell lines 5637 (RRID:CVCL_0126), UM-UC-3 (RRID:CVCL_A5WS), J82 (RRID:CVCL_0359), and T24 (RRID:CVCL_0554), as well as the immortalized uroepithelial cell line SV-HUC-1 (RRID:CVCL_3798) were purchased in 2018 from the Chinese Academy of Cell Resource Center (Shanghai, China), where they were authenticated with short tandem repeat profiling. All cells were maintained as previously described (23), tested negative for Mycoplasma with the PCR Mycoplasma Detection Kit (ABM, catalog no, G238) every 2 weeks and not cultured for longer than 15 passages.

Plasmids and molecular cloning

Plasmids for expression of wild-type (WT) METTL3 were kindly provided by Dr. Shuibin Lin (Sun Yat-sen University, Guangzhou, China; ref. 24). pDEST–PD-L1 plasmid for expression of WT PD-L1 was kindly provided by Dr. Jiadong Wang (Peking University, Beijing, China). A pGL3-PDL1–3′-UTR plasmid was modified from the pGL3 3′UTR reporter WT 1.3 kb CD274 Hs 3UTR Final (RRID: Addgene_107009; ref. 25) by replacing the original insert's UTR sequence downstream of the luciferase coding sequence with the PD-L1 3′-UTR sequence after the stop codon (1–1321bp, Cloned into XbaI and SalI sites).

Transfection and siRNA/short hairpin RNA–mediated knockdown

Transfection of plasmids was performed using Lipofectamine 2000 (Invitrogen, catalog no. 11668027) according to the manufacturer's instructions. Transient knockdown of target genes by siRNA was performed with Lipofectamine RNAi Max (Invitrogen, catalog no. 13778075). Sequences of siRNAs used in the study are listed in Supplementary Table S1. For short hairpin RNA (shRNA) knockdown, pLKO.1 constructs, together with packing and helper plasmids, pCMV delta R8.2 (RRID: Addgene_12263), and pCMV-VSV-G (RRID:Addgene_8454), were cotransfected into HEK293T cells (RRID:CVCL_0063). Lentiviral particles were collected at 48 hours and 72 hours posttransfection and then used to infect cells in presence of 6 μg/mL polybrene (Sigma-Aldrich, catalog no. TR-1003). Forty-eight hours after infection, 4 μg/mL puromycin (Sigma-Aldrich, catalog no. 58–58–2) was added to the culture medium to select the infected cells. The pLKO.1 lentiviral shRNA constructs targeting human METTL3 (shMETTL3–1, catalog no. RHS3979–201764032; shMETTL3–2, catalog no. RHS3979–201764033) and GFP (catalog no. RHS4459) were purchased from Dharmacon.

Detection of gene expression

For mRNA studies, total RNA from bladder cancer cells was extracted using TRIzol reagent (Invitrogen, catalog no. 15596026). Complementary DNA (cDNA) synthesis was performed with the Maxima H Minus cDNA Synthesis Master Mix kit (Invitrogen, catalog no. M1662) using 1 µg RNA per sample. qPCR reactions were performed using Power SYBR Green Master Mix (Thermo Fisher Scientific, catalog no. 4367659) to determine mRNA transcript level. For the RNA stability assay, bladder cancer cells were plated 24 hours before treatment with 2 μg/mL actinomycin D (AdooQ BioScience, catalog no. A13239) and harvested for mRNA quantification at the indicated time points after treatment. Primers for qRT-PCR are listed in Supplementary Table S2.

For Western blotting, protein was extracted from bladder cancer cells with RIPA buffer (Cell Signaling Technology, catalog no. 9806) following the standard protocol. Equal amounts of protein were then mixed with loading buffer and incubated at 100°C for 5 minutes and subjected to conventional Western blot analysis. Antibodies are listed in Supplementary Table S3. Paraffin sections of bladder cancer tissue samples from mice and human patients were antigen retrieved, blocked, and processed as described previously (26). IHC stains were performed using standard histology procedures. All immunostained sections were scanned at 20x magnification using a Pannoramic MIDI scanner (3DHISTECH, Hungary). In each section, staining intensity and frequency of positive cells were analyzed by ImageJ software with IHC Toolbox plug-in, according to the manufacturer's instructions (27). The final IHC score for each section was calculated using H score [0 × percentage of negatively stained cells) + 1 × percentage of lightly stained cells) + 2 × percentage of moderately stained cells) + 3 × percentage of strongly stained cells)], and scores varied from 0 to 300 where 300 represents 100% of cells strongly stained (28).

Bioinformatic analyses

For The Cancer Genome Atlas (TCGA) cohort, RNA sequencing data from 411 bladder cancer samples and 19 noncancerous bladder samples were collected from the Genomic Data Commons data portal (https://portal.gdc.cancer.gov). For Gene Expression Omnibus (GEO) datasets, the two biggest bladder cancer cohorts, GSE13507 (n = 223) and GSE31189 (n = 92) were downloaded from the GEO (http://www.ncbi.nlm.nih.gov/geo/). We increased the statistical power and leveraged cohorts with small sample size sufficiently by integrating the two datasets into a combined large cohort including 217 bladder cancer samples and 98 noncancerous bladder samples after log2-transformation, background correction, and quantile normalization. The batch effect was corrected using the combat function of the sva package (29) and principal component analysis was used to evaluate the batch effect removal. The bladder cancer samples were divided into two groups (METTTL3high and METTL3low) according to the median expression of METTL3. Gene Set Enrichment Analysis (GSEA) was conducted using the clusterProfiler R package (30). The code used for GSEA analysis has been uploaded to the Codeocean capsule as the following link: https://codeocean.com/capsule/8637897/tree/v1

M6A quantification

Global m6A levels in mRNA were quantified colorimetrically with an EpiQuik m6A RNA Methylation Quantification Kit (Epigentek, catalog no. P-9005) following the manufacturer's instructions.

To examine m6A modification of PD-L1 mRNA, mRNA was purified from total RNA of bladder cancer cells using the PolyATtract mRNA Isolation Systems (Promega, catalog no. Z5310) and then fragmented. One ninth of the mRNA was kept as an input sample, while four ninths of the mRNA were immunoprecipitated with control IgG or anti-m6A antibody (Synaptic Systems, catalog no. 202003). The immunoprecipitated RNA was washed, eluted, and concentrated with an RNA Clean and Concentrator kit (Zymo Research, catalog no. 1013). The immunoprecipitated RNA or input RNA from each sample was reverse transcribed into cDNA and analyzed by real-time PCR. The corresponding m6A modification of PD-L1 mRNA in each sample was calculated relative to the input using the following formula: %Input = 2^(CtInput – CtIP) × 4 × 100%. The primers for putative m6A modification sites (PDM) of PD-L1 mRNA are listed in Supplementary Table S2.

Chromatin immunoprecipitation assays

Chromatin immunoprecipitation (ChIP) assay was performed using a Simple ChIP Assay Kit (Cell Signaling Technology, catalog no. 9004) according to the manufacturer's instructions. Briefly, bladder cancer cells were first cross-linked with 1% formaldehyde for 10 minutes. Then glycine was added to a final concentration of 0.125 M and incubation was continued for 5 minutes. Afterward, cells were collected and lysed. After sonication, one eleventh of the lysate was kept as an input sample, while five elevenths of the lysate were immunoprecipitated with control IgG or anti–c-Jun antibody. Immunocomplexes from each sample were eluted, followed by treatment with RNase A and proteinase K. Both the input sample and the immunoprecipitated sample were analyzed by real-time PCR. The recruitment of c-Jun to METTL3 was calculated relative to the input using the following formula: %Input = 2^(CtInput – CtIP) × 5 × 100%.Three sets of primers (PR1–3) designed to cover the putative binding sites (BS) are listed in Supplementary Table S2 and antibodies used for ChIP assay are listed in Supplementary Table S5.

Luciferase reporter assay

Bladder cancer cells (3 × 104) were transfected with 400 ng firefly luciferase reporter plasmid pGL3-PDL1–3′-UTR and 40 ng Renilla luciferase reporter plasmid pRL-TK (Promega, catalog no. E2241). Both firefly and Renilla luciferase activities were assessed 48 hours posttransfection with the Dual-Luciferase Reporter Assay System (Promega, catalog no. E1910) and the luciferase activity was calculated by normalizing firefly luciferase activity for transfection efficiency against Renilla luciferase activity.

Lactate dehydrogenase–based cytotoxicity and apoptotic assay

CD8+ T cells (catalog no. PB009–3F-C) were obtained from AllCells (Shanghai) and cultured in ImmunoCult-XF T Cell Expansion Medium (STEMCELL Technologies, catalog no. 10981). Seventy-two hours after activation with ImmunoCult Human CD3/CD28/CD2 T Cell Activator (STEMCELL Technologies, catalog no. 10970) and 30 U/mL recombinant human IL2 (Shanghai Sangon Biotech, catalog no. C610004), CD8+ T cells were resuspended at 1×106 cells/mL in the culture medium and added to the preadherent bladder cancer cells at an effector-to-target cell ratio of 10:1. The T-cell mediated cytotoxicity on bladder cancer cells was determined by LDH assay. After coculture of CD8+ T cells and bladder cancer cells for 7 hours, the culture supernatants were collected. The release of lactate dehydrogenase (LDH) into the supernatants was measured as an indicator of cell lysis using the Cytotoxicity Detection Kit PLUS (Sigma-Aldrich, catalog no. 04744926001) according to the manufacturer's instructions. Effector cell control was determined by LDH released from T cells cultured in the absence of bladder cancer cells. Low control was determined by LDH released from bladder cancer cells cultured in the absence of T cells. High control was determined by LDH released from the bladder cancer cells treated with lysis reagent. Hence, specific cytotoxicity was calculated as cytotoxicity (%) = (test value - effector cell control - low control)/(high control - low control) × 100%. Bladder cancer cell apoptosis induced by CD8+T cells was assessed by Annexin V-PE/7-AAD staining. After coculture with CD8+ T cells for 7 hours, bladder cancer cells were double stained by Annexin V-PE and 7-AAD (BD Biosciences, catalog no. 559763) according to the manufacturer's protocol and then detected by flow cytometry. Unstained control cells and a single stain control for each fluorochrome were used to set up flow cytometric compensation. A region was established according to light-scattering properties of bladder cancer cells. The dot plot of Annexin V-PE versus 7-AAD was used for the assessment of apoptosis. Cells that were Annexin V-PE– and 7-AAD–negative were considered viable. Cells that were Annexin V-PE–positive and 7-AAD–negative were considered early apoptotic. Cells that were Annexin V-PE– and 7-AAD–positive were considered late apoptotic or already dead. Hence, apoptosis was assessed by adding proportions of cells in Annexin V-PE–positive/7-AAD–negative quadrant and Annexin V-PE–positive/7-AAD–positive quadrant together.

Mouse model

The N-butyl-N-4-hydroxybutyl Nitrosamine (BBN)-induced bladder carcinogenesis mouse and MB49 cell line–derived xenograft mouse models were established as previously described (31). Briefly, male C57BL/6J mice (6 weeks old) were given drinking water containing 0.05% (w/v) BBN (TCI, catalog no. B0938) for 20 weeks. After the BBN administration, mice were given normal drinking water and injected with 10% DMSO (as a control) or 20 mg/kg SP600125 (Selleck, catalog no. 129–56–6) i.p. every 3 days. After seven injections, mice were euthanized for tissue retrieval. For the bladder cancer cell-derived xenograft mouse model, male C57BL/6J mice (6 weeks old) were injected subcutaneously with 1 × 106 MB49 cells. One week after bladder cancer cell injection, 10% DMSO or 20 mg/kg SP600125 were injected i.p. every 3 days. After six injections, the control group had reached the humane endpoint of tumor size so the animal experiment was ended to guarantee laboratory animal welfare and all mice were euthanized for tissue retrieval. All animal procedures were performed under a protocol (LLSC20200322) approved by Anhui Medical University (Anhui, China) Institutional Animal Care and Use Committee and in accordance with NIH guide for the care and use of Laboratory animals (NIH Publications No. 85–23, revised 2011).

Statistics

Data are presented as the mean ± SD of more than three independent experiments. All statistical analyses were performed using Excel (Microsoft) or GraphPad Prism (GraphPad Software Inc.). The two-tailed Student t test or two-way Analysis of Covariance (ANCOVA) was used to calculate statistical significance. A P value of less than 0.05 was considered significant.

Data and materials availability

The authors declare that all relevant data are available within the article and its Supplementary information files or from the corresponding author upon reasonable request. The m6A-sequencing datasets have been submitted to the NCBI database under the accession number PRJNA498900.

METTL3 deficiency impairs m6A level and expression of PD-L1 in bladder cancer cells

We first examined the m6A RNA methylation status of total RNA from four bladder cancer cell lines (5637, J82, UM-UC-3, T24) and one immortalized normal urothelial cell line (SV-HUC-1) and found that m6A levels in all bladder cancer cell lines are significantly higher than those in SV-HUC-1 (Fig. 1A). Among the four bladder cancer cell lines, 5637 and J82 were selected for further study since RNA m6A levels were relatively higher in these two lines (Fig. 1A). In our previous study, we performed meRIP-sequencing and mapped the m6A methylomes in 5637 cells, and our data suggested that METTL3-mediated m6A modification was involved in the tumorigenesis of bladder cancer cells by regulating a panel of target genes and signaling pathways (21). Inspired by the fact that several immune response-related pathways (NF-κB, IFNγ, etc.) were enriched after METTL3 was knocked down, we were interested to know if METTL3 also played a role in regulating tumor immune response. Therefore we checked a panel of immune checkpoint genes (ICOSLG, CD86, CD80, PD-L1, PD-L2, RGMb, CTLA4, PD-1, CD28, LGALS9), among which, PD-L1 (encoded by CD274) is the only gene with significant m6A signal enrichment around the 3′-UTR that is reduced upon METTL3 knockdown (Fig. 1B). Also, among the key m6A regulators (METTL3, METTL14, WTAP, FTO, and ALKBH5), only knockdown of METTL3 has a significant and consistent effect on PD-L1 mRNA and protein levels in both 5637 and J82 cells (Fig. 1C and D; Supplementary Fig. S2A and S2B). We then transiently transfected two different siRNAs targeting METTL3 into 5637 and J82 cells (Fig. 1E), along with reporter plasmids containing the PD-L1 3′-UTR (1–1321bp after stop codon) immediately downstream of luciferase coding sequences. Luciferase activity is significantly reduced by knockdown of METTL3 (Fig. 1F). We also demonstrated that the downregulation of METTL3 in bladder cancer cells renders the cells more sensitive to T-cell–mediated tumor cell-killing using a LDH assay (Fig. 1G) and a cell apoptosis assay (Fig. 1H). Together, these findings suggest that the downregulation of METTL3 expression reduces PD-L1 expression, which in turn enhances the cytotoxicity of cocultured CD8+ T cells to bladder cancer cells in vitro.

Figure 1.

PD-L1 is regulated by METTL3-mediated m6A modification. A, mRNA m6A level in four bladder cancer cell lines (5637, J82, UM-UC-3, and T24) and one normal urothelial cell line, SV-HUC-1, was determined by RNA m6A quantification kit. B, The abundance of m6A on PD-L1 mRNA transcripts in 5637 cells as detected by m6A-seq was plotted using Integrative Genomics Viewer. The y-axis shows sequence read number, blue boxes represent exons, and blue lines represent introns. C and D, mRNA level (C) and protein level (D) of PD-L1 in 5637 and J82 cells transfected with indicated siRNAs were examined by qPCR and Western blot. E, Knockdown of METTL3 by two different siRNAs in 5637 and J82 cells was verified by qPCR. F, Relative luciferase activity of pGL3-PDL1–3UTR after cotransfection with indicated METTL3 siRNAs in 5637 or J82 cells. Firefly luciferase activity was measured and normalized to Renilla luciferase activity. G, Cytotoxicity of activated CD8+ T cells on cocultured 5637 or J82 cells transfected with METTL3 siRNAs. H, Analysis of 5637 or J82 cell apoptosis after coculture with activated CD8+ T cells by flow cytometry. Data are presented as mean ± SD of no less than three independent experiments, with individual data points shown. P values were assessed by two-tailed Student t test (A, C, and E–G) in comparison with SV-HUC-1 (A) or the Scramble group (C and E–G). *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 1.

PD-L1 is regulated by METTL3-mediated m6A modification. A, mRNA m6A level in four bladder cancer cell lines (5637, J82, UM-UC-3, and T24) and one normal urothelial cell line, SV-HUC-1, was determined by RNA m6A quantification kit. B, The abundance of m6A on PD-L1 mRNA transcripts in 5637 cells as detected by m6A-seq was plotted using Integrative Genomics Viewer. The y-axis shows sequence read number, blue boxes represent exons, and blue lines represent introns. C and D, mRNA level (C) and protein level (D) of PD-L1 in 5637 and J82 cells transfected with indicated siRNAs were examined by qPCR and Western blot. E, Knockdown of METTL3 by two different siRNAs in 5637 and J82 cells was verified by qPCR. F, Relative luciferase activity of pGL3-PDL1–3UTR after cotransfection with indicated METTL3 siRNAs in 5637 or J82 cells. Firefly luciferase activity was measured and normalized to Renilla luciferase activity. G, Cytotoxicity of activated CD8+ T cells on cocultured 5637 or J82 cells transfected with METTL3 siRNAs. H, Analysis of 5637 or J82 cell apoptosis after coculture with activated CD8+ T cells by flow cytometry. Data are presented as mean ± SD of no less than three independent experiments, with individual data points shown. P values were assessed by two-tailed Student t test (A, C, and E–G) in comparison with SV-HUC-1 (A) or the Scramble group (C and E–G). *, P < 0.05; **, P < 0.01; ***, P < 0.001.

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METTL3 regulates m6A enrichment and stability of PD-L1 mRNA

Multiple studies have demonstrated an association between m6A and mRNA decay. We hypothesized that METTL3 regulates PD-L1 expression by sustaining mRNA stability. We established a stable METTL3 knockdown model in both 5637 and J82 cells with specific shRNAs and found that both mRNA and protein levels of PD-L1 are significantly inhibited when METTL3 is depleted (Fig. 2A and B). When cells are treated with actinomycin D to block the synthesis of mRNA, PD-L1 mRNA stability is reduced upon METTL3 knockdown (Fig. 2C). In addition, in light of our meRIP-sequencing data, we predicted the existence of m6A modification sites around the stop codon of PD-L1 mRNA by SRAMP (http://www.cuilab.cn/sramp; ref. 32; Fig. 2D; Supplementary Table S4). Among all three predicted sites (PDM1–3), PDM3 has the highest confidence. We also selected a region indicated by neither meRIP-sequencing or a prediction tool (PDM0, -314to -212 bp before stop codon) for the negative control in an meRIP-qPCR assay. Our data revealed that m6A modification is enriched at the PDM3 site (116 bp after stop codon) and drastically reduced upon METTL3 knockdown (Fig. 2E).

Figure 2.

METTL3 sustains RNA stability and PD-L1 expression by regulating the m6A modification near the stop codon of PD-L1 mRNA. A and B, PD-L1 mRNA and protein levels in 5637 and J82 cells stably transfected with sh-METTL3–1, sh-METTL3–2) or with sh-GFP (control). C, mRNA level of PD-L1 in METTL3 stable knockdown bladder cancer cells treated with actinomycin D for indicated time (**, P < 0.01 equivalent ANCOVA). D, PDM near the stop codon of PD-L1 mRNA. E, Reduction of m6A modification in specific regions of PD-L1 transcripts upon METTL3 depletion was examined by gene-specific m6A-qPCR assay. F and G, mRNA (determined by qPCR) and protein levels of PD-L1 in 5637 and J82 cells transfected with indicated siRNAs targeting m6A readers. H and I, qPCR and Western blot showing decrease of PD-L1 expression in 5637 and J82 cells upon IGF2BP1 knockdown. J, mRNA level of PD-L1 in 5637 and J82 cells transfected with siRNAs targeting IGF2BP1 or a scramble sequence and treated with actinomycin D for indicated time. Data are presented as mean ± SD of three independent experiments, with individual data points shown (A, E, F, and H) or trend lines connecting mean values (C and J). P values were assessed by two-tailed Student t test (A, E, F, and H) or equivalent ANCOVA (C and J) in comparison with the sh-GFP group (A, C, and E) or the Scramble group (F, H, and J). *, P < 0.05; **, P < 0.01; ***, P < 0.001. TAA, thymine, adenine, adenine; at the end of CD274 mRNA coding sequence to stop the translation.

Figure 2.

METTL3 sustains RNA stability and PD-L1 expression by regulating the m6A modification near the stop codon of PD-L1 mRNA. A and B, PD-L1 mRNA and protein levels in 5637 and J82 cells stably transfected with sh-METTL3–1, sh-METTL3–2) or with sh-GFP (control). C, mRNA level of PD-L1 in METTL3 stable knockdown bladder cancer cells treated with actinomycin D for indicated time (**, P < 0.01 equivalent ANCOVA). D, PDM near the stop codon of PD-L1 mRNA. E, Reduction of m6A modification in specific regions of PD-L1 transcripts upon METTL3 depletion was examined by gene-specific m6A-qPCR assay. F and G, mRNA (determined by qPCR) and protein levels of PD-L1 in 5637 and J82 cells transfected with indicated siRNAs targeting m6A readers. H and I, qPCR and Western blot showing decrease of PD-L1 expression in 5637 and J82 cells upon IGF2BP1 knockdown. J, mRNA level of PD-L1 in 5637 and J82 cells transfected with siRNAs targeting IGF2BP1 or a scramble sequence and treated with actinomycin D for indicated time. Data are presented as mean ± SD of three independent experiments, with individual data points shown (A, E, F, and H) or trend lines connecting mean values (C and J). P values were assessed by two-tailed Student t test (A, E, F, and H) or equivalent ANCOVA (C and J) in comparison with the sh-GFP group (A, C, and E) or the Scramble group (F, H, and J). *, P < 0.05; **, P < 0.01; ***, P < 0.001. TAA, thymine, adenine, adenine; at the end of CD274 mRNA coding sequence to stop the translation.

Close modal

The effect of m6A modification on mRNA transcripts requires that these modifications be recognized by m6A binding proteins known as m6A readers. We knocked down all YTHDFs (YTHDF1/2/3 and YTHDC1/2) and IGF2BPs (IGF2BP1/2/3) in 5637 and J82 cells. Among these tested reader proteins, knockdown of IGF2BP1 significantly decreases the mRNA and protein levels of PD-L1 in both bladder cancer cells (Fig. 2FI). Furthermore, as with METTL3 knockdown, the stability of PD-L1 mRNA is also reduced when IGF2BP1 is silenced (Fig. 2J). Taken together, our findings suggest that METTL3 promote PD-L1 expression by regulating the RNA m6A modification at the 3′-UTR, which could be recognized by IGF2BP1 and is essential for mRNA stability.

JNK MAPK signaling is associated with METTL3 expression in bladder cancer tissue

We and others have previously reported that m6A and METTL3 expression levels are upregulated in bladder cancer tissue (21, 33). To explore the mechanism associated with aberrant expression of METTL3 in bladder cancer, we next performed in silico studies in TCGA (http://cancergenome.nih.gov) bladder cancer dataset. Datasets with 19 PT samples and 411 bladder cancer samples that were further divided into high METTL3 group (n = 185) and low METTL3 group (n = 185) were selected and analyzed by GSEA. We screened signaling pathways associated with METTL3 expression from 11 core signaling pathways that are commonly involved in tumorigenesis (MAPK, MYC, NF-κB, Hippo, Hedgehog, Wnt, TP53, JAK/STAT, NOTCH, PI3K/AKT, TGFβ/SMAD). Among the tested signaling pathways, only the MAPK pathway is enriched in both tumor tissues compared with PT tissues and high METTL3 expressing tumors compared with low METTL3 expressing tumors (Fig. 3AC). Similar enrichment of the MAPK pathway was observed when GSEA was performed in GEO datasets (combined from two largest bladder cancer datasets with both tumor and normal samples, GSE31189 & GSE13507, n = 223 and 92, respectively. Fig. 3C). MAPK is a complicated signaling network and functions mainly through p38 (MAPK14), JNK, and ERKs (34). We validated the activity of MAPK signaling in bladder cancer tumor and normal tissues by examining samples from a cohort of 35 patients with bladder cancer and samples from a previously established BBN-induced bladder carcinogenesis mouse model (20). Analysis by IHC showed that the phosphorylation of both JNK1 and c-Jun is dramatically elevated in human bladder cancer tissues (versus PT samples, Fig. 3D; Supplementary Fig. S1) and in mice with invasive bladder cancer (n = 12) compared with normal bladder tissue (n = 10, Fig. 3E). There is no significant difference in MAPK14 activation, and activation of ERK even decreased in mouse tumor tissues, as indicated by phosphorylated MAPK14 and ERK staining (Supplementary Fig. S2C and S2D). These results suggest that JNK/MAPK signaling is activated in bladder cancer and may be associated with METTL3 upregulation.

Figure 3.

Signaling pathways associated with METTL3 expression in bladder cancer. A and B, Ranked lists (A) and bubble plot (B) show 11 pathways analyzed by GSEA of TCGA bladder cancer dataset. C, Enrichment plots show the MAPK pathway analyzed by GSEA of TCGA bladder cancer dataset (top) and GEO datasets (bottom). NES, normalized enrichment score. D and E, Quantitative measurement of phosphorylated c-Jun and JNK1 in human (D)/mice (E) bladder tumor tissues (n = 35 for human samples; n = 12 for mice samples) and PT tissue (n = 35 for PT; n = 10 for normal tissues). Immunostaining was quantitatively measured using ImageJ analysis software. Data are presented as mean ± SD of independent samples with individual data points shown and P values were assessed by two-tailed Student t test in comparison with the noncancerous group (D and E). **, P < 0.01; ***, P < 0.001. p-JNK1, phosphorylated JNK1; phosphorylated c-Jun, p-c-Jun; NOM P value, nominal P value.

Figure 3.

Signaling pathways associated with METTL3 expression in bladder cancer. A and B, Ranked lists (A) and bubble plot (B) show 11 pathways analyzed by GSEA of TCGA bladder cancer dataset. C, Enrichment plots show the MAPK pathway analyzed by GSEA of TCGA bladder cancer dataset (top) and GEO datasets (bottom). NES, normalized enrichment score. D and E, Quantitative measurement of phosphorylated c-Jun and JNK1 in human (D)/mice (E) bladder tumor tissues (n = 35 for human samples; n = 12 for mice samples) and PT tissue (n = 35 for PT; n = 10 for normal tissues). Immunostaining was quantitatively measured using ImageJ analysis software. Data are presented as mean ± SD of independent samples with individual data points shown and P values were assessed by two-tailed Student t test in comparison with the noncancerous group (D and E). **, P < 0.01; ***, P < 0.001. p-JNK1, phosphorylated JNK1; phosphorylated c-Jun, p-c-Jun; NOM P value, nominal P value.

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Inhibiting JNK signaling reduces the METTL3 expression and global RNA m6A level in bladder cancer cells

We next investigated the role of JNK signaling in regulating METTL3 expression in bladder cancer cells. Treatment with JNK inhibitor SP600125 dramatically reduces the m6A levels in both 5637 and J82 cells (Fig. 4A). In addition, SP600125 could decrease the mRNA and protein level of the m6A writer METTL3 in a dose-dependent manner while having little effect on METTL14 expression (Fig. 4B and C). There are three members of JNK family, and the next question is which play key roles in regulating m6A modification in bladder cancer cells. We knocked down JNK1, JNK2, and JNK3 by transfecting siRNAs specifically targeting these three genes, respectively (Supplementary Fig. S2E). The results revealed that knockdown of JNK1 has the greatest effect on inhibiting m6A levels (Fig. 4D; Supplementary Fig.S2F) and METTL3 expression in both 5637 and J82 cells (Fig. 4E and F).

Figure 4.

JNK regulates METTL3 expression in bladder cancer cells. A, Global RNA m6A level of 5637 and J82 cells treated with SP600125 was assessed by an m6A quantification kit. B and C, mRNA (determined by qPCR; B) and protein (determined by Western blot; C) levels of METTL3 in 5637 and J82 cells treated by indicated dose of SP600125. DF, RNA m6A level (D), mRNA level (E), and protein level (F) of METTL3 in 5637 and J82 cells after knockdown of indicated JNKs were also plotted. G, Putative binding site of c-Jun in METTL3 promoter region. H and I, ChIP assay showed the recruitment of c-Jun at METTL3 promoter in 5637 and J82 cells treated with SP600125 (H) or transfected with indicated siRNAs (I). Data are presented as mean ± SD of three independent experiments, with individual data points shown. P values were assessed by two-tailed Student t test in comparison with the MOCK group, which was treated with the vehicle (DMSO; A and B) or the Scramble group (D, E, I, and H). *, P < 0.05; **, P < 0.01.

Figure 4.

JNK regulates METTL3 expression in bladder cancer cells. A, Global RNA m6A level of 5637 and J82 cells treated with SP600125 was assessed by an m6A quantification kit. B and C, mRNA (determined by qPCR; B) and protein (determined by Western blot; C) levels of METTL3 in 5637 and J82 cells treated by indicated dose of SP600125. DF, RNA m6A level (D), mRNA level (E), and protein level (F) of METTL3 in 5637 and J82 cells after knockdown of indicated JNKs were also plotted. G, Putative binding site of c-Jun in METTL3 promoter region. H and I, ChIP assay showed the recruitment of c-Jun at METTL3 promoter in 5637 and J82 cells treated with SP600125 (H) or transfected with indicated siRNAs (I). Data are presented as mean ± SD of three independent experiments, with individual data points shown. P values were assessed by two-tailed Student t test in comparison with the MOCK group, which was treated with the vehicle (DMSO; A and B) or the Scramble group (D, E, I, and H). *, P < 0.05; **, P < 0.01.

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c-Jun is the key downstream transcriptional factor in JNK signaling (34). An analysis of the METLL3 promoter region was performed using the software PROMO v3.0.2, (which utilizes TRANSFAC v8.3, http://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3; refs. 35, 36). The analysis found four potential binding sites for c-Jun (BS1, BS2, BS3, and BS4, Fig. 4G; Supplementary Table S5) and these were utilized for further testing by ChIP validation (Supplementary Fig. S3). The results showed direct binding of c-Jun to BS1 (−283 to −277 bp) of the METTL3 promoter is suppressed upon SP600125 treatment (Fig. 4H) and knockdown of JNK1 (Fig. 4I). The above data leads us to conclude that JNK signaling promotes the aberrant expression of METTL3 and results in hyper-m6A methylation in bladder cancer.

METTL3 reinstates the PD-L1 expression in bladder cancer cells blocked by JNK signaling

We further explored whether JNK signaling regulates PD-L1 expression through METTL3 by examining the mRNA and protein levels of PD-L1 in SP600125-treated bladder cancer cells. Consistent with our observations of METTL3, PD-L1 mRNA levels are strongly decreased after treatment (Fig. 5A; Supplementary Fig. S4A). Moreover, protein levels of PD-L1 are also reduced in a SP600125 dose dependent manner (Fig. 5B). We also found that siRNA knockdown of JNK1 could diminish the expression of PD-L1 in both 5637 and J82 cells (Fig. 5C and D; Supplementary Fig. S4B). Luciferase reporter assay indicated that activity of PD-L1 3′-UTR is significantly suppressed by knockdown of JNK1 (Fig. 5E and F; Supplementary Fig. S4C and S4D). Moreover, knockdown of JNK1, but not by other JNKs (Fig. 5G; Supplementary Fig. S4E), or treatment with SP600125 (Fig. 5H; Supplementary Fig. S4F) also decreases m6A levels at the PDM3 site of PD-L1 mRNA. To further validate the association among JNK1 activity, expression of METTL3, and PD-L1, we performed a Pearson correlation analysis of these genes in 35 human bladder cancer samples. Phosphorylated JNK1 determined by IHC was found significantly correlated with METTL3 protein level (r = 0.757, P < 0.0001) and PD-L1 protein level (r = 0.547, P = 0.0007), while there's a weak correlation (r = 0.313) between METTL3 and PD-L1 and the P value shows a certain trend toward significance (P = 0.067; Supplementary Fig. S4G).

Figure 5.

JNK regulates m6A and expression level of PD-L1. AD, qPCR and Western blot showing PD-L1 expression in bladder cancer cells treated with SP600125 or siRNAs targeting different JNKs. E, Knockdown of JNK1 by two different siRNAs in 5637 cells verified by qPCR. F, Relative luciferase activity of pGL3-PDL1–3UTR after cotransfection with JNK1 siRNAs into 5637 cells. Firefly luciferase activity was measured and normalized to Renilla luciferase activity. G and H, Reduction of m6A modification in specific regions of PD-L1 transcripts upon knockdown of indicated JNK isoforms (G) and JNK signaling inhibition (H) were examined by gene-specific m6A-qPCR assay. I, Analysis of 5637 cell apoptosis while transfected with indicated siRNAs and cocultured with/without activated CD8+ T cells by flow cytometry. J, Cytotoxicity of activated CD8+ T cells on cocultured 5637 cells transfected with indicated siRNAs. Data are presented as mean ± SD of three independent experiments, with individual data points shown. P values were assessed by two-tailed Student t test in comparison with the control group, which was treated with the vehicle (DMSO; A and H) or the Scramble group (C, E–G, I, and J). *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 5.

JNK regulates m6A and expression level of PD-L1. AD, qPCR and Western blot showing PD-L1 expression in bladder cancer cells treated with SP600125 or siRNAs targeting different JNKs. E, Knockdown of JNK1 by two different siRNAs in 5637 cells verified by qPCR. F, Relative luciferase activity of pGL3-PDL1–3UTR after cotransfection with JNK1 siRNAs into 5637 cells. Firefly luciferase activity was measured and normalized to Renilla luciferase activity. G and H, Reduction of m6A modification in specific regions of PD-L1 transcripts upon knockdown of indicated JNK isoforms (G) and JNK signaling inhibition (H) were examined by gene-specific m6A-qPCR assay. I, Analysis of 5637 cell apoptosis while transfected with indicated siRNAs and cocultured with/without activated CD8+ T cells by flow cytometry. J, Cytotoxicity of activated CD8+ T cells on cocultured 5637 cells transfected with indicated siRNAs. Data are presented as mean ± SD of three independent experiments, with individual data points shown. P values were assessed by two-tailed Student t test in comparison with the control group, which was treated with the vehicle (DMSO; A and H) or the Scramble group (C, E–G, I, and J). *, P < 0.05; **, P < 0.01; ***, P < 0.001.

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Given that the JNK pathway is involved multiple cellular processes and has pleiotropic effects on cancer, we wanted to further investigate if the impact of JNK on immune response of bladder cancer cell to T cells mainly relies on METTL3-modulated PD-L1 m6A modification and expression. We knocked down JNK1, METTL3, and PD-L1 in 5637 and J82 cells by specific siRNAs respectively, and cell viability/apoptosis of bladder cancer cells cocultured with/without T cells were examined. Our data suggest that besides knockdown of JNK1 and METTL3 knockdown of PD-L1 also mildly increases apoptosis of bladder cancer cells cocultured without T cells. When bladder cancer cells were cocultured with activated CD8+ T cells, depletion of JNK1, METTL3, or PD-L1 all show more significant effects on increasing apoptosis (compared with T-cell–free counterparts) than control treatment (Fig. 5I; Supplementary Fig. S5A–S5C). LDH assay also suggested that bladder cancer cells are more sensitive to T-cell–mediated tumor cell-killing when PD-L1 is knocked down (Fig. 5J; Supplementary Fig. S5D). On the other hand, despite the inhibition of bladder cancer cell proliferation upon knockdown of METTL3 or JNK1, silencing PD-L1 has little effect on bladder cancer cell growth (Supplementary Fig. S5E). Together, these findings suggest that the downregulation of JNK signaling reduces PD-L1 expression and sensitizes bladder cancer cells to T-cell cytotoxicity.

We next performed a rescue assay and found that overexpression of METTL3 using a plasmid rescues the decreased mRNA level (Fig. 6A; Supplementary Fig. S6A), protein level (Fig. 6B; Supplementary Fig. S6B), m6A RNA level of PD-L1 (Fig. 6C; Supplementary Fig. S6C), and the luciferase activities of the PD-L1 3′-UTR reporter plasmid (Fig. 6D; Supplementary Fig. S6D), thus reversing the effects of SP600125. Finally, ectopic expression of METTL3 also attenuates the promoting effect of SP600125 on the cytotoxicity of cocultured T cells to 5637 and J82 cells (determined by LDH assay and apoptotic assay, Fig. 6E and F; Supplementary Fig. S6E and S6F). Moreover, we knocked down JNK1 with an RNAi strategy and then overexpressed METTL3 in bladder cancer cells (Supplementary Fig. S6G and S6H). Ectopic expression of METTL3 attenuated the promoting effect of JNK1 knockdown on the cytotoxicity of activated T cells to bladder cancer cells as well, which was determined by LDH assay (Fig. 6G; Supplementary Fig. S6I) and apoptotic assay (Fig. 6H; Supplementary Fig. S6J and S6K). These data suggest that the effect of JNK signaling on PD-L1 expression relies on its regulation of METTL3 expression and thereby the m6A modification of PD-L1.

Figure 6.

METTL3 recovers PD-L1 expression depressed by JNK inhibition in bladder cancer cells. AD, qPCR analysis (A), Western blot (B), m6A-qPCR assay (C), and luciferase assay (D) indicate PD-L1 expression and m6A level in 5637 cells where JNK inhibition was rescued by exogenous METTL3. E, Cytotoxicity of activated CD8+ T cells on cocultured 5637 cells was measured by LDH assay. F, Analysis of 5637 cell apoptosis while cocultured with activated CD8+ T cells by flow cytometry. GL, Cytotoxicity of activated CD8+ T cells on cocultured 5637 cells (G,I, and K) and cell apoptosis of 5637 cells ( H ,J, and L) transfected with indicated plasmid or siRNAs was measured by LDH assay and flow cytometry, respectively. Data are presented as mean ± SD of three independent experiments, with individual data points shown. P values for the differences between indicated groups were assessed by two-tailed Student t test. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 6.

METTL3 recovers PD-L1 expression depressed by JNK inhibition in bladder cancer cells. AD, qPCR analysis (A), Western blot (B), m6A-qPCR assay (C), and luciferase assay (D) indicate PD-L1 expression and m6A level in 5637 cells where JNK inhibition was rescued by exogenous METTL3. E, Cytotoxicity of activated CD8+ T cells on cocultured 5637 cells was measured by LDH assay. F, Analysis of 5637 cell apoptosis while cocultured with activated CD8+ T cells by flow cytometry. GL, Cytotoxicity of activated CD8+ T cells on cocultured 5637 cells (G,I, and K) and cell apoptosis of 5637 cells ( H ,J, and L) transfected with indicated plasmid or siRNAs was measured by LDH assay and flow cytometry, respectively. Data are presented as mean ± SD of three independent experiments, with individual data points shown. P values for the differences between indicated groups were assessed by two-tailed Student t test. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

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In addition, we knocked down PD-L1 in METTL3 stable overexpression bladder cancer cells (Supplementary Fig. S7A), and overexpressed PD-L1 in METTL3 stable knockdown bladder cancer cells (Supplementary Fig. S7B). In both conditions, PD-L1 could rescue the effect of METTL3 overexpression/knockdown on T-cell cytotoxicity of bladder cancer cells (Fig. 6IL; Supplementary Fig. S8A–S8F). Collectively, these results indicate m6A-mediated PD-L1 expression is the key regulator of bladder cancer cell survival at presence of T cells, but it does not affect bladder cancer growth.

JNK inhibition impairs PD-L1 expression and tumor progression in vivo

Two immunocompetent mouse models of bladder cancer were employed to analyze the effect of JNK signaling on the immune escape potential in vivo. We first injected murine bladder cancer MB49 cells into C57BL/6J mice subcutaneously. One week after the primary injection, mice were randomly divided into two groups and subjected to SP600125 treatment (n = 16) or DMSO as control (n = 16). Tumor volume was monitored every 3 days. As expected, we observed retarded tumor formation, lighter tumor weight, and smaller tumor volume (Fig. 7AC) in the SP600125-treated group when compared with the control group. Administration of SP600125 also effectively suppressed tumor progression in the BBN-induced bladder cancer model. When tumor samples from both groups were subjected to pathologic examination, we found 6 mice in DMSO-treated groups developed dysplasia–carcinoma in situ (CIS), and the rest 15 mice progressed to muscle-invasive bladder cancer (MIBC). In SP600125-treated group, only 5 mice progressed to MIBC and the rest 18 mice developed dysplasia-CIS (Fig. 7D and E). Besides, compared with the DMSO-treated group, there is a significant reduction in relative gross bladder/body weight ratio in mice treated by SP600125 (Fig. 7F). Tumor samples were then subjected to IHC staining. Consistent with our in vitro data, activation of JNK1 and c-Jun (determined by phosphorylated antibodies) and expression of METTL3 and PD-L1 are suppressed by SP600125 in both MB49 tumors (Fig. 7G and H) and BBN-induced tumors (Fig. 7I and J), and active caspase-3 (Ac-Caspase 3, an indicator for cell apoptosis) was elevated upon SP600125 treatment in both models (Fig. 7GJ). These data show that the JNK inhibitor SP600125 could effectively reduce PD-L1 expression and enhance immune response of bladder cancer in vivo.

Figure 7.

Inhibiting JNK signaling impairs bladder cancer progression in vivo. AC, Tumor weight (A), tumor growth curve of xenografts (B), and representative image of the tumors at the end of the experiment (C) from C57BL/6J mice, which were subcutaneously injected with murine bladder cancer MB49 cells and then subjected to SP600125 (n = 16) or DMSO (n = 16) treatment. D and E, Representative hematoxylin and eosin staining of tumors (D) and histopathologic changes in bladder sections (E) from C57BL/6J mice induced by BBN for 20 weeks and further subjected to SP600125 (n = 23) or DMSO (n = 21) treatment. F, G/B ratios calculated as (gross bladder weight/body weight) × 100% were plotted. G–J, Immunostaining for phosphorylated JNK1 (p-JNK1), phosphorylated c-Jun (p-c-Jun), METTL3, PD-L1, and cleaved caspase-3 (Ac-Caspase 3) in bladder tumor xenografts (G and H) and BBN-induced tumors (I and J) was quantitatively measured (three sections from every tumor tissue) and analyzed using ImageJ image analysis software. Scale bar, 50 μm. Data are presented as mean ± SD with individual data points shown and P values were assessed by two-tailed Student t test (A and FJ) or equivalent of ANCOVA (B) in comparison with the DMSO group. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 7.

Inhibiting JNK signaling impairs bladder cancer progression in vivo. AC, Tumor weight (A), tumor growth curve of xenografts (B), and representative image of the tumors at the end of the experiment (C) from C57BL/6J mice, which were subcutaneously injected with murine bladder cancer MB49 cells and then subjected to SP600125 (n = 16) or DMSO (n = 16) treatment. D and E, Representative hematoxylin and eosin staining of tumors (D) and histopathologic changes in bladder sections (E) from C57BL/6J mice induced by BBN for 20 weeks and further subjected to SP600125 (n = 23) or DMSO (n = 21) treatment. F, G/B ratios calculated as (gross bladder weight/body weight) × 100% were plotted. G–J, Immunostaining for phosphorylated JNK1 (p-JNK1), phosphorylated c-Jun (p-c-Jun), METTL3, PD-L1, and cleaved caspase-3 (Ac-Caspase 3) in bladder tumor xenografts (G and H) and BBN-induced tumors (I and J) was quantitatively measured (three sections from every tumor tissue) and analyzed using ImageJ image analysis software. Scale bar, 50 μm. Data are presented as mean ± SD with individual data points shown and P values were assessed by two-tailed Student t test (A and FJ) or equivalent of ANCOVA (B) in comparison with the DMSO group. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

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The global abundance of m6A and expression levels of its regulators, including writers, erasers, and readers, are often dysregulated in various types of cancers, and are associated with cancer progression and poor clinical outcome (16). In our previous study, we showed that overexpression of METTL3 promotes bladder cancer tumorigenesis and progression by regulating the mRNA m6A levels in MYC, RELA, IKBKB, and AFF4 (21, 37). However, the cause of METTL3 dysregulation in bladder cancer was still not known. Evidence has shown that cigarette smoke condensate could reduce the DNA methylation of the METTL3 promoter and subsequently recruit the transcription factor NFIC to activate the expression of METTL3 in pancreatic cancer (38). And P300 could mediate acetylation at lysine 27 of histone H3 (H3K27ac) and promote METTL3 transcription in gastric cancer (39). But the mechanism for this aberrant METTL3 expression in tumor tissue remained unclear. Here we demonstrate that JNK signaling is upregulated in bladder cancer and this promotes METTL3 transcription via c-Jun. Tobacco smoking is the main risk factor for bladder cancer (40). In fact, the carcinogen BBN mimics the effect of cigarette smoke and was used to establish the bladder cancer model in mice (41). Moreover, cigarette smoke could activate JNK signaling in urothelial cells (42) and bronchial epithelial cells (43). Thus, tobacco smoking might play a crucial role in activating JNK signaling and METTL3 expression of bladder cancer. However, this still awaits further investigation.

It is well-known that JNK signaling promotes tumor development and survival in many cancers (44). In bladder cancer, JNK signaling promotes cancer cell resistance to chemotherapy by inducing autophagy (45, 46). There is broad cross-talk between JNK signaling and other pathways, such as NF-κB, Wnt, and PI3K, that share common upstream activators and may act synergistically to regulate cancer cell survival (34). This evidence makes JNK signaling an attractive target for therapeutic intervention. However, there are at least three JNK isoforms with different functions in some types of cancer. SP600125 lacks specificity and selectivity for these JNK isoforms (47). Our data indicates that JNK1 is the main regulator mediating METTL3 expression and m6A abundance of PD-L1. In this study, we found that despite the mild direct effect of JNK and METTL3 on bladder cancer cell viability, m6A-mediated PD-L1 expression is the key regulator of bladder cancer cell survival in the presence of T cells. Therefore, selective inhibitors of JNK1 such as the isoquinolone derivative methylsulfonyl may become an appropriate therapeutic strategy of cancer treatment. More importantly, considering the promotional effect of JNK1 on both tumor growth and immune escape, drugs targeting JNK1 may provide stronger anticancer capability with lower dosage, less side effects, and a lower chance of drug resistance.

PD-L1 is a well-studied, coinhibitory immune checkpoint protein that is overexpressed in many types of cancer. Blocking the PD-L1/PD-1 pathway has been shown to have remarkable antitumor effects in patients with advanced cancers, including bladder cancer. But the response rate of anti–PD-L1 treatment is still limited. Therefore, furthering our understanding of the regulatory mechanisms for PD-L1 can bring substantial benefits to patients by improving the efficacy of current PD-L1/PD-1 blockade treatments. Regulation of PD-L1 expression has been summarized by Cha and colleagues, including genomic alternations, transcriptional, posttranscriptional, and posttranslational mechanisms (19). However, the understanding of RNA modification of PD-L1 mRNA was still limited. Here we report for the first time that METTL3 is essential in mediating the m6A modification of PD-L1 mRNA. This modification occurs near the stop codon region and is recognized by IGF2BP1. It is important for sustaining mRNA stability and the expression of PD-L1.

We knocked down several m6A readers to screen for the key regulator of PD-L1 expression and observed diverse consequences in two bladder cancer cells (Fig. 2F and G). The dynamic formation and distribution of m6A varies highly in different cells, and recognition by an expanding list of readers, which act in response to different stimuli, further increases the complexity. Although some of the reader proteins share similar domain structures, recent evidence still shows that they function differently in regulating gene expression. There is also cross-talk or competition between m6A reader proteins and even between the eraser FTO and readers during certain cellular responses. These proteins form a network of physical or functional interactions (48). Despite not systematically studied, the context-dependent function of m6A reader proteins have been shown in embryonic development (49). Thus, it is not surprising to find the varying consequences when different m6A reader proteins were knocked down in bladder cancer cells. We selected IGF2BP1 to investigate the standard/general m6A function axis in regulating the PD-L1 expression because it demonstrated similar functions in both 5637 and J82 cells.

RNA m6A modification has been found to play an essential role in various aspects of immunity, including immune recognition, activation of innate and adaptive immune responses, and cell fate decisions (17). Recently, Yang and colleagues reported that knockdown of the m6A eraser FTO sensitizes melanoma cells to IFNγ and anti–PD-1 treatment. It does this by increasing RNA decay in critical protumorigenic melanoma cell-intrinsic genes including PD-1 (PDCD1), CXCR4, and SOX10 through the m6A reader YTHDF2 (50). On the other hand, inhibition of the m6A writers METTL3 and METTL14 also enhanced the response to anti–PD-1 treatment in mismatch-repair-proficient or microsatellite instability-low (pMMR-MSI-L) colorectal cancer and melanoma by increasing cytotoxic tumor-infiltrating CD8+ T cells and their function (51). In addition, mRNA m6A methylation promotes dendritic cell activation (52) as well as mediating the cross-presentation of tumor antigens and the cross-priming of CD8+ T cells (53). However, we were interested to know if mRNA m6A methylation also plays a role in immune response from the aspect of tumor cells. In this study, we found PD-L1 could rescue the effect of METTL3 overexpression/knockdown on T-cell cytotoxicity of bladder cancer cells. Despite our previous finding that METTL3 promotes bladder cancer proliferation and invasion by regulating the mRNA m6A levels in MYC, RELA, IKBKB, and AFF4s (21, 37), knockdown of PD-L1 has little influence on bladder cancer proliferation in vitro. Our findings regarding METTL3-mediated m6A methylation of PD-L1 mRNA reveal new aspects of the function of RNA methylation in cancer immune therapy. Therapeutics targeting m6A may regulate the immune response by mediating both PD-L1 expression in cancer cells and the desired function of immune cells such as dendritic cell and CD8+ T cells.

We found that the suppression of JNK signaling reduced expression of METTL3 and m6A methylation of PD-L1, thus sensitizing bladder cancer cells to cytotoxicity by CD8+ T cells. This study demonstrates the essential role of JNK-mediated m6A methylation in the maintenance of tumor resistance to immunotherapy. The inhibition of JNK signaling and METTL3 provides the opportunity to overcome this barrier in bladder cancer immunotherapy.

M. Wu reports grants from National Natural Science Foundation of China during the conduct of the study. Q. Gao reports grants from National Natural Science Foundation of China during the conduct of the study. Y. Li reports grants from National Natural Science Foundation of China and Provincial Natural Science Foundation of Anhui during the conduct of the study. No disclosures were reported by the other authors.

Z. Ni: Data curation, formal analysis, investigation, methodology. P. Sun: Data curation, formal analysis, investigation, methodology. J. Zheng: Resources, validation. M. Wu: Funding acquisition, methodology. C. Yang: Data curation, software, investigation, methodology. M. Cheng: Resources, methodology. M. Yin: Investigation. C. Cui: Investigation. G. Wang: Investigation. L. Yuan: Resources. Q. Gao: Funding acquisition, project administration, writing–review and editing. Y. Li: Conceptualization, supervision, funding acquisition, writing–original draft.

The authors thank the Center for Scientific Research of Anhui Medical University and Facilities of School of Life Science for valuable help in their experiment.

This work was supported by National Natural Science Foundation of China (grant nos. 81872313 and 81672776 to Y. Li, 82003000 to M. Wu, and 82172893 to Q. Gao) and Distinguished Young Scholars of Anhui Province (2108085J39 to Y. Li).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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Supplementary data