DENR controls JAK2 translation to induce PD-L1 expression for tumor immune evasion

Chen, Baiwen; Hu, Jiajia; Hu, Xianting; Chen, Huifang; Bao, Rujuan; Zhou, Yatao; Ye, Youqiong; Zhan, Meixiao; Cai, Wei; Li, Huabin; Li, Hua-Bing

doi:10.1038/s41467-022-29754-y

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Published: 19 April 2022

DENR controls JAK2 translation to induce PD-L1 expression for tumor immune evasion

Baiwen Chen^1,2^na1,
Jiajia Hu³^na1,
Xianting Hu¹^na1,
Huifang Chen²,
Rujuan Bao²,
Yatao Zhou²,
Youqiong Ye ORCID: orcid.org/0000-0001-8332-4710²,
Meixiao Zhan⁴,
Wei Cai⁵,
Huabin Li¹ &
…
Hua-Bing Li ORCID: orcid.org/0000-0003-3573-6803^2,5,6

Nature Communications volume 13, Article number: 2059 (2022) Cite this article

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Abstract

RNA-binding proteins (RBPs) can recognize thousands of RNAs that help to maintain cell homeostasis, and RBP dysfunction is frequently observed in various cancers. However, whether specific RBPs are involved in tumor immune evasion by regulating programmed death ligand-1 (PD-L1) is unclear. Here, we perform targeted RBP CRISPR/Cas9 screening and identify density regulated re-initiation and release factor (DENR) as a PD-L1 regulator. DENR-depleted cancer cells exhibit reduced PD-L1 expression in vitro and in vivo. DENR depletion significantly suppresses tumor growth and enhances the tumor-killing activity of CD8⁺ T cells. Mechanistically, DENR antagonizes the translational repression of three consecutive upstream open reading frames (uORFs) upstream of Janus kinase 2 (Jak2); thus, DENR deficiency impairs JAK2 translation and the IFNγ-JAK-STAT signaling pathway, resulting in reduced PD-L1 expression in tumors. Overall, we discover an RBP DENR that could regulate PD-L1 expression for tumor immune evasion, and highlight the potential of DENR as a therapeutic target for immunotherapy.

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Introduction

Programmed death ligand 1 (PD-L1) is typically expressed on tumor cells and immune cells such as macrophages and dendritic cells, and the interaction between PD-L1 and its receptor, programmed cell death-1 (PD-1), on T cells inhibits the tumor-killing activity of these cells^1,2,3. One of the most exciting progressions in cancer research in recent years has been the development of the PD1/PD-L1 blockade, which benefits patients with diverse types of cancers^4,5,6. However, only a small proportion of patients can benefit from anti-PD-1/PD-L1 therapy⁷. Those tumors with high PD-L1 expression are with poor prognosis but considered to be more sensitive to PD-1/PD-L1 blockade⁸. Nevertheless, the mechanism that regulates PD-L1 expression in tumor cells has not been fully elucidated, understanding of which regulatory network would aid to develop potential therapeutic strategies to enhance efficacy of anti-tumor immunotherapy.

PD-L1 expression can be induced by inflammatory factors such as interferon (IFN)γ, IFNα, IFNβ, tumor necrosis factor (TNF)α, and transforming growth factor (TGF)β. IFNγ, which is generally produced by T cells, is considered to be the most prominent inducer of PD-L1⁹. The binding of IFNγ to its receptors activates the JAK-STAT signaling pathway, thereby up-regulating the expression of a series of genes, including PD-L1⁹. Besides inflammatory signaling, the elevated expression of oncogenic transcription factors, such as MYC¹⁰, HIF1α¹¹, NF-κB¹², and BRD4¹³, in cancers has also been demonstrated to directly regulate PD-L1 transcription. In addition, increasing evidence demonstrated that PD-L1 also undergoes different post-transcriptional regulation. CKLF-like MARVEL transmembrane domain-containing 6 has been shown to positively regulate PD-L1 by direct binding to increase its half-life^14,15, and miR-513¹⁶ and miR-155¹⁷ suppress PD-L1 expression by directly binding to the 3ʹ UTR of PD-L1 mRNA.

RNA-binding proteins (RBPs) are involved in all aspects of RNA metabolism, including splicing, exportation, location, modification, translation, and decay¹⁸. Although PD-L1 regulation has been extensively studied at the transcriptional and post-transcriptional levels, whether a particular RBP is involved in the PD-L1 regulation networks is still unknown. To systematically discover the involvement of the RBP regulatory pathways in PD-L1 expression, we perform targeted pooled RBP CRISPR/Cas9 screening and identify an RBP, density-regulated re-initiation and release factor (DENR), as a PD-L1 regulator. We find three upstream open reading frames (uORFs) in the Janus kinase 2 (Jak2) 5ʹ UTR region that constitutively repress the translation of JAK2. DENR binds to 40S ribosomes and bypasses the three uORFs to reinitiate the translation of the Jak2 main ORF. A deficiency in DENR leads to decreased JAK2 translation and impairs JAK-STAT signaling, thus reducing PD-L1 expression in tumor cells and repressing tumor growth by enhancing the anti-tumor killing activity of CD8⁺ T cells in the tumor microenvironments.

Results

Pooled CRISPR screening identified DENR as a regulator of PD-L1

Previously, we successfully performed the pooled RBP CRISPR screening of macrophages and identified m⁶A “writers” to be positive regulators of TNF expression and macrophage activation¹⁹. To unbiasedly and systematically screen for post-transcriptional regulators of PD-L1, we constructed a new customized RBP CRISPR mini-library with a larger RBP pool than our previous 782-RBP-gene CRISPR mini-library. The new version of the RBP library contained 10 single-guide (sg)RNAs per gene of 1467 known RBPs²⁰, as well as positive and negative control sgRNAs (Supplementary Data 1). We used fluorescence-activated single-cell sorting (FACS) to quantify the PD-L1 expression levels, which served as a reliable and convenient CRISPR screening readout.

IFNγ is generally considered to be the most potent inducer of PD-L1 expression¹⁴, and murine macrophage-like RAW264.7 cells show a rapid response to IFNγ treatment, expressing high levels of surface PD-L1 (Supplementary Fig. 1a). We constructed a Cas9-expressing stable RAW264.7 cell line, then infected the cells with the above lentivirus RBP CRISPR library at a multiplicity of infection of 0.3 (Fig. 1a and Supplementary Fig. 1b) and selected them with puromycin for 7 days. Next, the cells were stimulated with IFNγ for 4 h to induce PD-L1 expression at a moderate level (Supplementary Fig. 1a), to allow easier identification of RBP genes that could either enhance or suppress PD-L1 expression as previously suggested. Then, the cells were sorted by FACS based on their surface expression of PD-L1 (Fig. 1b). Genomic DNA was extracted from PD-L1^High, PD-L1^Low, and unsorted cells, and the inserted sgRNAs were amplified and sequenced (Fig. 1b).

**Fig. 1: Identification of DENR as a PD-L1 regulator by CRISPR/Cas9 screening.**

As expected, sgRNAs targeting known positive regulators of PD-L1, such as JAK1, JAK2, and STAT1 in the IFNγ signaling pathway⁷, were identified in the PD-L1^Low cells and no negative control sgRNAs were enriched (Fig. 1c). Among the top candidates in the PD-L1^low cells, three components of the SWI/SNF chromatin remodeling complex, BRD2, BRD9, and SMARCD1, were identified (Fig. 1c). This complex has been reported to regulate the expression of PD-L1^21,22. These findings demonstrated the validity of our screening setup.

To further validate our screening results, we tested the top hit genes enriched in PD-L1^low cells by generating stable KO RAW264.7 cell lines using individual corresponding sgRNAs. The FACS results showed that Knocking-out those genes, Denr, Brd2, Atp5sl, Kat7, Ndufa10, all significantly reduced the expression of cell surface PD-L1 (Supplementary Fig. 1d). Noticeably, DENR knockout in Cas9-RAW264.7 cells could markedly reduce the total and surface PD-L1, which could be further validated by additional three specific sgRNAs (Fig. 1d, e). Collectively, we identified DENR to be a posttranscriptional regulator of PD-L1 by customized pooled RBP CRISPR screening.

DENR regulates PD-L1 through the JAK-STAT signaling pathway

DENR is reported to be an RBP that binds directly to transfer RNAs in vitro²³ and is involved in the translation re-initiation of target mRNAs with short ‘start-stop’ uORFs (e.g., AUGUGA)²⁴. However, we did not find any classical conserved ‘start-stop’ ORF in the short 5ʹ UTR of the PD-L1 gene, suggesting indirect PD-L1 regulation by DENR. To test our hypothesis, we knocked out DENR gene in murine colon adenocarcinoma MC38 cells and assayed the protein and mRNA levels of PD-L1 by immunoblot, FACS and RT-qPCR respectively (Fig. 2a–c). In the absence of IFNγ, PD-L1 protein levels were almost undetectable (Fig. 2a), and there was no difference in mRNA levels between DENR KO and control cells (Fig. 2c). IFNγ treatment, in contrast, dramatically decreased both total and surface PD-L1 protein and mRNA levels in DENR KO cells compared to control cells (Fig. 2a–c), suggesting that DENR does not directly regulate PD-L1 translation but indirectly regulates it through IFNγ-induced transcriptional signaling pathways. In addition, we observed a reduction of PD-L1 after DENR KO in murine melanoma B16/F10 cells (Supplementary Fig. 2a, b), suggesting DENR’s general role in reducing PD-L1 expression in different cell lines.

**Fig. 2: DENR depletion impaired the JAK-STAT signaling pathway.**

To identify the potential signaling pathway involved, we harvested IFNγ-treated control and DENR KO MC38 cells for RNA-seq. Bioinformatic analysis of the RNA-seq data found that 114 genes, including PD-L1, were significantly downregulated in DENR KO cells (Fig. 2d). KEGG analysis showed that these downregulated genes in DENR KO cells were enriched in JAK-STAT and PD-L1 signaling pathways (Fig. 2e), which could be verified by GESA analysis (Fig. 2f). In addition to PD-L1, the top down-regulated genes in DENR KO cells included Cxcl10, Cxcl9, Cxcl1, and Irf1 (Fig. 2d), which are known to be specifically regulated by JAK-STAT signaling in response to IFNγ^25,26,27.

It has been thoroughly documented that IFNGR1 and IFNGR2 sense extracellular IFNγ, then transduce signals into the cell cytoplasm to activate JAK1 and JAK2 by phosphorylation^9,28. The activated JAK1 and JAK2 subsequently phosphorylate STAT1 in most cells and STAT3 in some cells, and the pSTAT1/3 are translocated into the nucleus, where they bind to and promote the transcription of target genes such as PD-L1⁹. We thus hypothesized that DENR KO impaired the JAK-STAT signaling pathway. To test our hypothesis, we analyzed the protein and phosphorylation levels of STAT1 and STAT3 (Fig. 2g). Interestingly, we found the phosphorylation levels, but not the total protein levels, of STAT1 and STAT3 were dramatically reduced after IFNγ treatment, suggesting that the DENR-targeted gene was upstream of STAT1/3. Together, our data demonstrate that DENR regulates PD-L1 expression via the JAK-STAT signaling pathway.

DENR directly regulates JAK2 expression

To further explore the molecular mechanisms of DENR function in PD-L1 regulation, we determined the protein and phosphorylation levels of all upstream factors of STAT1/3, including IFNGR1, IFNGR2, JAK1, and JAK2 (Fig. 3a and Supplementary Fig. 2c), by western blotting. We found that both the protein and phosphorylation levels, but not the mRNA levels of JAK2 were markedly reduced in DENR KO cells cultured with or without IFNγ treatment (Fig. 3a–c), while JAK1, IFNGR1, and IFNGR2 were unaffected by DENR depletion (Fig. 3a and Fig. 2d), indicating DENR’s specific translational regulation over JAK2 expression. In addition, the reduction in JAK2 protein levels could be restored by the overexpression of an sgRNA-resistant DENR cDNA expression vector in DENR KO cells (Supplementary Fig. 2e), thus excluding the potential off-targeting effects of JAK2 by DENR sgRNAs. Furthermore, individually knocking out JAK1, JAK2, STAT1 showed a similar reduction of total and surface PD-L1 protein levels comparing to Knocking out DENR, but not STAT3 (Fig. 3d, e). These results were consistent with our CRISPR screening that JAK1, JAK2, STAT1 but not STAT3 sgRNAs were enriched in PD-L1^low cells (Fig. 1c). Together, our data prove that DENR directly regulates JAK2 expression via translation, thus its downstream JAK2-STAT1 signaling pathway and then PD-L1 expression.

**Fig. 3: JAK2 is translationally controlled by DENR.**

As an RBP, DENR likely regulates JAK2 at the post-transcriptional level, but it may also regulate JAK2 activity by protein-protein interactions, thus, we performed immunoprecipitation (IP) and found that DENR pulled down Multiple Copies In T-Cell Lymphoma-1 (MCTS1) (Supplementary Fig. 3a), a known DENR interaction partner protein. However, JAK2 was not detected as an immunoprecipitate with the anti-DENR antibody (Supplementary Fig. 3a). Reciprocally, JAK2 IP with the anti-JAK2 antibody pulled down STAT3 as expected, but not DENR (Supplementary Fig. 3a).

DENR is reported to regulate the translation of target mRNAs^29,30,31, and we found that the protein, but not the mRNA levels of JAK2 were reduced in DENR KO cells. In addition, we excluded the possibility of a protein–protein interaction between DENR and JAK2. All together, these data prompt us to hypothesize that DENR directly regulates the translation of JAK2, which in turn drives PD-L1 expression in response to IFNγ.

uORFs inhibit the translation of JAK2 and are antagonized by DENR

We next sought to understand how DENR regulates JAK2 translation. DENR has been reported to regulate the translation of target genes with uORFs^29,30. Interestingly, we observed three conservative uORFs in both the murine and human JAK2 gene, with two copies of a short ‘start-stop’ uORF, AUGUGA, and a longer uORF, AUGUUCUGA (Fig. 4a). Furthermore, knocking out DENR in human cancer cell lines, such as human colon HT29 cancer cells and human melanoma A375 cells, also resulted in reduced JAK2 protein levels (Supplementary Fig. 2d), indicating that the mechanism of DENR in regulating JAK2 expression is conservative in both murine and human.

**Fig. 4: DENR regulation of the translation of JAK2 is dependent on three uORFs.**

To test the function of the uORFs upstream of the Jak2 gene, we cloned the wildtype (WT) (three intact uORFs) or mutant (no uORF or only one longer uORF) Jak2 5ʹ UTRs into EGFP-HA-SV40 Poly(A) reporter constructs (Fig. 4b) which were transfected into WT B16/F10 cells, then measured the EGFP fluorescence by microscopy and the HA-tagged EGFP protein levels by western blot (Fig. 4c, d). The expression levels of the reports with three or one uORF were markedly repressed compared to that of the reporter lacking uORF, and the reporter with three uORFs was repressed more than the reporter with only one uORF (Fig. 4c, d). These data indicate that the uORFs upstream Jak2 mRNA are indeed responsible for the repression of JAK2 translation efficiency.

Next, to determine whether the repressive function of the uORFs in Jak2 mRNA is further regulated by DENR, reports were transfected into control and DENR KO cells (Fig. 4e). The proportion of EGFP-positive cells was reduced by up to 50% in DENR KO cells transfected with the reporters of three uORFs and by up to 15% in cells containing the report with one uORF, while reports with no uORF were not repressed by DENR depletion (Fig. 4f). In addition, the corresponding mRNA levels of EGFP reports were measured and no differences were observed in all experimental groups (Fig. 4g), suggesting that the difference in EGFP fluorescence is not due to EGFP transcription, but the translational regulatory effects of the upstream uORFs and associated DENR.

Furthermore, we analyzed the global genes and identified 2635 genes with at least 1 uORF. We then overlap those genes with translation deficiency genes defined from published ribosome profiling sequencing data set of DENR KO NIH3T3 cells (GSE116221)³², the results showed there are 106 genes with at least 1 uORF and decreased translation efficiency, and Jak2 was among them (Fig. 4h).

Together, these results show that the uORFs upstream Jak2 mRNA inhibits the translation of JAK2, and the inhibitory effects of uORFs are directly antagonized by DENR.

DENR deficiency stalls ribosomes between uORFs

To explore the detailed molecular mechanisms of how DENR directly regulates JAK2 expression via upstream uORFs, we performed RNA immunoprecipitation with an anti-DENR antibody to detect DENR-bound RNAs (Fig. 5a). Unexpectedly, the qPCR amplification of the immunoprecipitated RNAs showed that DENR was bound to not only Jak2 mRNAs, but also Jak1 and Gapdh mRNAs lacking uORFs (Fig. 5b). Recent structural studies have shown that DENR binds to 40S ribosomes^33,34,35, thus we performed polysome fractionation analysis to determine whether DENR affects the translation efficiency of all or only certain mRNAs. Our results showed that DENR depletion did not affect the formation of global polyribosomes (Fig. 5c). In addition, we carried out RT-qPCR on each polysome fraction in Control and DENR KO cells, and the results showed that Jak1 mRNAs without uORFs did not have any significant ribosome association changes with each fractions (Fig. 5d), however, the distribution of Jak2 mRNAs displayed a leftward shift on the fractionation gradient (Fig. 5e), indicating that Jak2 mRNAs associated with smaller polysomes upon DENR depletion, suggesting that the translation efficiency of JAK2 but not JAK1 were suppressed in DENR KO cells. Finally, we also reanalyzed a ribosome footprint sequencing data set (GSE140084)³⁰ in DENR KO versus WT HeLa cells and found that loss of DENR led to the accumulation of 40S and 80S on uORFs in JAK2 (Fig. 5f) but not JAk1 (Supplementary Fig. 3b). Together, our results suggest that DENR binds all mRNAs and bypass the uORFs when encountering them, thus promote translation efficiency of specific mRNAs with uORFs, specifically Jak2 mRNAs.

**Fig. 5: DENR depletion stalls ribosomes on uORFs.**

Next, to further confirm that the global protein synthesis was not affected by DENR KO, we first performed Click-iT OPP Protein Synthesis assay and found no changes of the global incorporation efficiency of OP-puro analog into newly translated peptides in DENR KO over WT cells (Supplementary Fig. 3c, d). Then, we checked the phosphorylation status of translation initiation factors eIF2α, 4EBP1, and eEF2, whose phosphorylation would lead to protein synthesis inhibition^36,37. The results showed that the phosphorylation status of eIF2α, 4EBP1, and eEF2 was un-altered in DENR KO cells (Supplementary Fig. 3e).

All together, these data exclude the possibility that DENR has a broad effect on the translation of all mRNAs and suggest that DENR constitutively associates with ribosomes, travels indiscriminately along all mRNAs, promotes translation re-initiation only when encountering uORFs belonging to certain mRNAs.

DENR depletion attenuates PD-L1 expression and tumor growth in vivo

So far, we have shown the molecular mechanism of how DENR regulates the translation of JAK2 to control the expression of PD-L1. Next, we sought to determine the in vivo role of DENR in tumor progression and PD-L1 expression. We first showed that DENR depletion did not affect tumor growth in culture dishes by Cell Counting Kit 8 (CCK8) assay (Supplementary 5a, b). Then, EGFP-DENR KO and tdTomato-Control WT MC38 cells were evenly mixed and subcutaneously implanted into the flanks of WT C57BL/6 mice, so that the KO and WT tumor cells grew in the same tumor immune microenvironment, eliminating the extrinsic effects. We monitored the tumor volume daily, and selected mice at the early tumor growth time point (day 8) and at the late tumor regression time point (day 12) for sacrifice to analyze the ratio of DNER KO to control tumor cells (Fig. 6a). Interestingly, we found that the percentage of DENR KO tumor cells in all tumors was similar to that of control WT tumor cells on day 8 but much lower than that of WT tumor cells on day 12 (Fig. 6b), indicative of a specific and faster clearance of DENR KO tumor cells in the same tumor microenvironment. When analyzed by FACS, the PD-L1 expression levels of DENR KO tumor cells were significantly lower than those of control WT cells (Fig. 6c).

**Fig. 6: DENR depletion reduces tumor growth and PD-L1 expression in vivo.**

As CD8⁺ T cells are central to tumor killing, and the anti-tumor immunity of CD8⁺ T cells was inhibited by the expression of PD-L1 in the tumor microenvironment³⁸, we assess the infiltration and function of the CD8⁺ T cells in two syngeneic tumor models of both B16/F10 and MC38 tumor cells. We subcutaneously injected DENR KO B16/F10 or MC38 cells into the lower flanks of WT C57BL/6 mice, monitored the daily tumor growth, and sacrificed the mice at the end of the experiment. As expected, the DENR KO tumors grew much slower than WT tumors, and the tumor size of the KO cells was much smaller for B16/F10 tumor (Fig. 6d). More dramatically, MC38 tumors almost did not grow with DENR depletion (Fig. 6e). When B16/F10 tumors were analyzed by FACS, we observed that the percentage of CD4⁺ T cells among the immune cell population was similar in the KO and control tumors, the percentage of CD8⁺ T cells was significantly higher in DENR KO tumors than WT tumors (Fig. 6f, g). Next, to prove that the more efficient killing of DENR KO tumors over WT tumors was indeed due to increased tumor infiltration and enhanced killing of CD8⁺ T cells, we subcutaneously implanted DENR KO and control cells into the flanks of RAG1−/− mice in which the T cells and B cells were depleted. We observed that the growth of DENR KO and WT tumors in the RAG1−/− mice no longer showed significant differences without the existence of T cells for B16/F10 tumors, and slight difference for MC38 tumors (Supplementary Fig. 5c, d).

It is established that PD-L1 in tumors inhibits tumor immune clearance³⁸, and our results showed that DENR KO dramatically attenuated tumor growth with decreased PD-L1 expression and increased CD8⁺ T cells infiltration. Together, our data indicate that DENR expression in tumors supports tumor evasion by regulating PD-L1 expression, and DENR KO assists faster tumor immune ejection, suggesting that DENR is a promising immunotherapy target.

DENR correlates with patient survival in immunotherapy

As tumor cells rely on the expression of PD-L1 to resist attack by CD8⁺ T cells³⁸, we assess the direct tumor-killing ability of CD8⁺ T cells by co-cultured with DENR KO or control MC38 cancer cells. The results showed that in OVA expressing cancer cells, DENR depletion resulted in weaker p-STAT1 signaling, lower PD-L1 expression, and significant higher percentage of apoptotic cells (Fig. 7a–c and Supplementary Fig. 5e–g). Notably, adding anti-PD-1 to the co-culture to block PD-L1 also increased the apoptotic population of control cells (Fig. 7a, b). Together, these results suggest that DENR depletion in tumors reduces the expression of PD-L1 upon CD8⁺ T cell treatment and, thus, enhances CD8⁺ T cell anti-tumor activities.

**Fig. 7: DENR depletion enhances the anti-tumor killing ability of CD8⁺ T cells.**

In clinical studies, anti-PD1/PD-L1 therapy has benefited patients with diverse types of cancers. However, the low response rates to PD1/PD-L1 therapy in some tumor types and patients highlight the urgent clinical need to identify other immunotherapy targets or combination strategies⁷. To evaluate whether DENR can serve as a potential target or marker in anti-PD1/PD-L1 therapy, we analyzed three independent clinical studies of melanoma patients under anti-PD-1^39,40, or anti-CTLA-4⁴¹ treatment regimens (Fig. 7d–f). We found that lower DENR expression in tumors correlated with better survival outcomes of patients given anti-PD-1 therapies (Fig. 7d, e), but not with anti-CTLA-4 therapy (Fig. 7f), demonstrating the potential benefits of using DENR as an immunotherapy target or marker.

Discussion

RBPs play an essential role in RNA metabolism, and aberrant RNA-RBP networks are related to cellular dysfunction and human diseases^{42,43,44,45,46}. However, which RBPs affect tumor immune evasion by regulating PD-L1 is largely unknown. Here, we described identifying RBP-DENR as a regulator of PD-L1 by employing pooled RBP CRISPR-Cas9 screening. Mechanistically, we found three uORFs in the 5′ UTR of JAK2, and loss of DENR stalled the 40S ribosomes on these uORFs and reduced the translation of JAK2, which in turn impaired the JAK2-STAT1 signaling pathway and repressed PD-L1 expression in vitro and in vivo (Fig. 7g).

JAK2 is involved in a subset of cytokine receptor signaling pathways, including those involved in cell growth, development, and differentiation, such as growth hormone; erythropoietin; type-II receptors, IFNα, IFNβ, and IFNγ; and a wide range of interleukins⁴⁷. JAK2V617F is a highly recurrent mutation in myeloproliferative diseases, in which the normal valine on the 617th amino acid in the coding region is replaced by phenylalanine⁴⁸. This change activates JAK2 and improves PD-L1 expression, mediating abnormal proliferation and immune escape in myeloproliferative neoplasms⁴⁸. We observed that DENR depletion almost abolished the translation of JAK2, suggesting DENR could be a potential target in JAK2V617F-mediated diseases. Furthermore, as the three uORFs found in JAK2 constitutively repressed the translation of JAK2, and with clinical sequence research reporting an uORF mutation of JAK2 in a patient with chronic lymphocytic leukemia⁴⁹, it appears the mutation of uORFs in JAK2 is a high-risk factor, and more in-depth sequencing of the 5ʹ UTR is needed in patients with myeloproliferative diseases.

In our study, we have identified that DENR positively regulates PD-L1 expression through IFNγ-JAK2-STAT1 signaling, DENR high patients are less sensitive to PD-L1/PDL1 immune check blockade (ICB) therapy. The results seem inconsistent to some previous study showing some PD-L1 high patients were more sensitive to PD-L1/PDL1 blockade. However, PD-L1/PDL1 blockade therapy only benefit ~20% cancer patients, and among the remaining patients, many of them have mid-high PD-L1 expression⁷. In Tumor Microenvironment, a major source of IFNγ are T cells. Infiltrating T cells secrete more IFNγ, which stimulate tumor cells via JAK-STAT signaling to overexpress PD-L1, as well as many other known and unknown molecules, to inhibit T cell function⁵⁰. This could explain that only ~ 20% patients could benefit from ICB. It has been well studied that interfering with tumor IFN-JAK-STAT signaling combined with PD-1/PD-L1 immune check blockade (ICB) monotherapy was more effective than only ICB monotherapy⁵⁰. Our current study proves that DENR directly regulates JAK2, and downregulation of DENR will inhibit IFN-JAK-STAT signaling and its downstream PD-L1 and many other IFN response genes. Thus, DENR low patients with weakened JAK-STAT signaling could be more sensitive to ICB therapy. Multiple previous studies also have reported that candidate genes positively regulate tumor expression of PD-L1 is associated with PD-1 blockade resistance^51,52. However, the clinical data is limited in our study, and the DENR function in tumor microenvironment of the tumor patients is very complex and is in need of further investigation.

DENR associates ribosomes but does not have the specific RNA binding motif that is seen in other RBPs^53,54. However, the function of DENR is also highly correlated with particular uORF RNA sequences²⁴. Consistent with our findings, some studies reported that DENR shows the highest regulation efficiency at start-stop uORFs and weaker efficiency at longer uORFs²⁴, and the Kazok sequence before uORFs and penultimate codons are also involved^24,30. This suggests the mechanisms of translation re-initiation of ribosomes to overcome uORFs are complex and may involve multiple factors; therefore, further study is needed.

DENR is highly expressed in multiple tumor types and indicates the poor prognosis of patients⁵⁵. In this study, we uncovered the function of DENR in regulating PD-L1 expression in immune evasion, and the results indicate that DENR is a potential therapeutic target, either in isolation or in combination with current PD-L1/PD-1 blockade therapies.

Methods

Mice

All the wild-type mice at 6- to 8-week-old of age were maintained under specific pathogen-free conditions, and all animal procedures were performed in ethical compliance and with approval by the Institutional Animal Care and Use Committee (IACUC) of Shanghai Jiao Tong University School of Medicine.

Cell culture

HEK293T, RAW264.7, MC38, B16/F10, HT29, and A375 cells were obtained from ATCC. Cells were cultured in RPMI-1640 or DMEM supplemented with 100 IU/ml Penicillin, 100 μg/ml Streptomycin and 10% fetal calf serum.

Cloning

sgRNA oligos were synthesized and cloned into the LentiGuide-puro (#52963, Addgene) or LentiCRISPR v2-Blast (#52963, Addgene) via the two BsmBI sites. The EGFP-sgRNA plasmid in this study was a gift from Jiyu Tong, and we replaced the EGFP with a tdTomato to generate a tdTomato-sgRNA plasmid. The WT Jak2 5ʹ UTR (NM_008413.4) and mutations were synthesized and cloned into pCMV-EGFP-HA-C (P0916, MiaoLing Plasmid Sharing Platform) by EasyGeno Assembly Cloning Kit (VI201, Tiangen) according to the manufacturer’s recommendations.

Lentiviral production and transduction

Lentivirus was produced by transfection of HEK293T cells with a transfer vector of sgRNA expression plasmids or lentiCas9-Blast (#52962, Addgene), and the packaging plasmids psPAX2 (#12260, Addgene) and pMD.G (#12259, Addgene) at a 4:3:1 ratio. Transfection was performed using Roche reagent as recommended by the manufacturer. The viral supernatant was collected at 48 h and 72 h following transfection, filtered through a 0.45 μm filter, and added to target cells.

CRISPR screen

A 10 sgRNA-per-gene CRISPR/Cas9 deletion library was designed to target 1467 RBP genes using CRISPR-FOCUS (cistrome.org/crispr-focus/), the library contained negative control sgRNAs: no binding sites in the genome, and positive control sgRNAs in IFNγ pathway (Supplementary Data 1). RAW264.7 cells were transduced with a lentiviral vector encoding Cas9 and selected with blasticidin (5 µg/ml) for 7 days. 5 × 10⁷ Cas9-expression RAW264.7 cells were infected with the pooled lentiviral RBP sgRNA library at a multiplicity of infection of 0.3 and selected with 5 μg/ml puromycin for 72 h, commencing 48 h after transduction. Rare PD-L1 low and PD-L1 high cells were enriched by FACS sorting at day 7. For the sorts, cells were pre-treated with 20 ng/ml IFNγ for 4 h, stained with PE-conjugated anti-PD-L1 antibody for 20 min, and washed with PBS prior to sorting for PD-L1 low and high cells. Genomic DNA was extracted from both the sorted cells and the unsorted cells. sgRNA sequences were amplified by two rounds of PCR, and purified for sequencing on Illumina Hiseq.

Immunoblotting and immunoprecipitation

For western blotting analysis, cells were lysed in RIPA buffer (P0013B, Beyotime) containing protease and phosphatase Inhibitor cocktail, EDTA free (78441, Thermo Fisher Scientific) for 30 min on ice followed by pelleting of insoluble material by centrifugation. Protein concentration was determined using a BCA assay. For co-immunoprecipitation experiments, 10 million cells were lysed in 1 ml NP40 buffer (P0013F, Beyotime) containing protease and phosphatase Inhibitor cocktail, EDTA free, for 30 min on ice. After concentration, supernatants were incubated with Rabbit anti-DENR, Rabbit anti-JAK2, or Rabbit normal IgG for 4 h at 4 °C, followed by addition of 25 µl protein A/G beads (B23201, Bimake) and incubation for a further 2 h. After five washes in NP40 buffer, beads were eluted in 100 μl IgG elution buffer, PH2.0 (21028, Thermo Fisher Scientific) and neutralized with 15 μl 50 mM NaOH.

Crosslinking and RNA-IP

MC38 cells were grown to confluency in 10 cm dish, cells were washed twice with cold PBS and irradiate uncovered with 0.2 Jcm⁻² total energy of 245 nm UV light. Cells in one dish were then scraped in 1 ml RNA-IP lysis buffer (25 mM Tris-HCl (pH 7.5), 1% NP-40, 150 mM NaCl, 1.5 mM MgCl2, 1 mM DTT, Protease and Phosphatase Inhibitor Cocktail, EDTA-free and 100 U/ml RNase inhibitor (Vazyme)). Lysates were sonicated (30 s on/30 s off) for 3 cycles (Bioruptor^® Pico sonication device) and cleared by centrifugation and split into input (100ul) and IP samples (800ul). The IP samples were incubated with 2 μg anti-DENR or Rabbit normal IgG for 4 h at 4 °C. Immunocomplexes were precipitated by incubation with protein A/G beads for 2 h at 4 °C. Beads were washed three times with low salt wash buffer (25 mM Tris-HCl (pH 7.5), 0.05% NP-40, 150 mM NaCl, 1.5 mM MgCl2 and 100 U/ml RNase inhibitor), followed by 3 washes with high salt wash buffer (25 mM Tris-HCl (pH 7.5), 0.05% NP-40, 300 mM NaCl, 1.5 mM MgCl2 and 100 U/ml RNase inhibitor). RNA in input or IP samples was eluted by Trizol reagent according to the manufacturer’s recommendations. 2 μl RNA were subjected to reverse transcription using TransScript All-in-One First-Strand cDNA Synthesis SuperMix (AT341-03, Transgen) according to the manufacturer’s instructions, followed by qPCR analysis.

In vitro killing assay

MC38 cells with or without OVA expression were infected with Cas9 and sgRNAs expression lentivirus to generate stable MC38 ± OVA Control or DENR KO cells. The spleen of OT-1 mice was dissociated and cultured with 5 µg/ml OVA _(257–264) peptides and 4 ng/ml IL-2 for three days, and changed to fresh media contain 4 ng/ml IL-2 for another three days. After amplification, CD8⁺ T cells were purified by negative selection with magnetic beads (8804-6825-74, ThermoFisher). After purification, 6 × 10⁵ CD8⁺ T cells were coculture with 2 × 10⁵ MC38-OVA Control or DENR KO cells (Effector: Target ratio: 3:1) in 24 wells plates, anti-PD1 (10 µg/ml) was added in MC38-OVA Control cells as a positive control. The cells were harvested after 16 h and labeled with 7-AAD, Caspase-3/7 or PD-L1 to detect by FACS, the gating strategy was shown as in Supplementary Fig. 4b.

Flow cytometry

Cells from the plates or tumors were isolated and resuspended in PBS plus 1% FBS and 5 mM EDTA. After staining with the fluorescently conjugated antibodies, flow cytometry data were acquired on BD LSRFortessa X-20 or Accuri C6 Plus and analyzed using FlowJo software (Tree Star), the gating strategy in analysis tumor tissues were shown as in Supplementary Fig. 4a.

Tumor models

Female WT C57BL/6 mice (6–8 weeks) and female RAG1^−/− mice (6–8 weeks) were purchased from Shanghai Model Organisms Center, the animals were maintained in specific-pathogen-free facilities. MC38 or B16/F10 cells were washed and resuspended in PBS at 5 × 10⁶/ml, mice were injected with 100 µl of total 5 × 10⁵ cells in the flank subcutaneously of WT C57BL/6 mice. Tumors were measured every 1–2 days (length × width) with a digital caliper. RAG1^−/− mice were given 1 × 10⁵ control or DENR KO cells, and tumors were measured every day. Tumor volume was determined using the formula: 0.52 × L × W². Mice were sacrificed before tumors ulceration, and in the study, all the tumor diameters did not exceed 20 mm that was the maximal tumor diameter permitted by the IACUC of Shanghai Jiao Tong University School of Medicine. For mixture EGFP-DENR KO and tdTomato-Control cells, 5 × 10⁵ EGFP and 5 × 10⁵ tdTomato cells were mixed and injected in the flank subcutaneously, tumors were measured every day, volumes were determined as described.

Antibodies

The antibodies used in the study were listed in the Supplementary Table 1.

sgRNA and RT-qPCR sequence

The oligonucleotides for sgRNA cloning and PCR primers used in the study were listed in the Supplementary Tables 2 and 3.

RNA extraction for RNA-seq and quantitative real-time PCR

For RNA-seq, MC38 control and DENR KO cells were stimulated with IFNγ (20 ng/ml) for 4 h and total RNA was extracted using TRIZOL (Invitrogen #15596026). RNA purification, reverse transcription, library construction, and sequencing were performed by Novogene Co., Ltd. (Shanghai, China) according to the manufacturer’s instructions (Illumina). Briefly, mRNA was purified from total RNAs by using poly-T oligo-attached magnetic beads and fragmented by the fragmentation buffer. First strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase, then using RNase H to degrade the RNAs. Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and dNTP. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3′ ends of DNA fragments, adaptor with hairpin loop structure were ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 370–420 bp in length, the library fragments were purified with AMPure XP system (Beckman Coulter, Beverly, USA). Then PCR amplification, the PCR product was purified by AMPure XP beads, and the library was finally obtained. The library was initially quantified by Qubit2.0 Fluorometer, then diluted to 1.5 ng/μl, and the insert size of the library is detected by Agilent 2100 bioanalyzer. After the library is qualified, the different libraries are pooled according to the effective concentration and the target amount of data off the machine, then being sequenced by the Illumina NovaSeq 6000. The end reading of 150 bp pairing is generated.

For quantitative real time PCR, total cellular RNA or RNA in polysome fraction was extracted using TRIZOL (Invitrogen #15596026). mRNA was reversely transcribed into cDNA using HiScript III 1st Strand cDNA Synthesis Kit (Vazyme, R312-01). Fluorescence real-time PCR was performed with the ChamQ Universal SYBR qPCR Master Mix (Vazyme, q711-02) with the CFX384 Touch Real-Time PCR Detection System (Bio-Rad, USA). β-actin was used as an internal control for normalization.

Statistical analysis

Survminer_0.4.9 package was used to determine the cutoff point of survival information for each dataset based on the association between DENR expression and patient overall survival. To find the maximum rank statistic and reduce the calculated batch effect, the “surv-cutpoint” function was used to dichotomy DENR expression, and all potential cutting points were repeatedly tested, then the patient samples were divided into the high-DENR group and the low-DENR group according to the maximum selected log-rank statistics. Kaplan–Meier comparative survival analyses for prognostic analysis were generated, and the log-rank test was used to determine the significance of the differences. For GSEA analysis the gene set “c2.all.v7.5” was downloaded from the MSigDB database. All statistical analysis was two-side and considered P < 0.05 as statistical significance.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The RNA-seq data generated in this study have been deposited in the GEO database under accession code GSE183980. The CRISPR screen data generated in this study have been deposited in the GEO database under accession GSE184048. For GSEA analysis the gene set “c2.all.v7.5” was downloaded from the MSigDB database [http://www.gsea-msigdb.org/gsea/index.jsp]. The remaining data are available within the Article, Supplementary Information or Source Data file. Source data are provided with this paper.

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Acknowledgements

We thank Kaiqiong Mao, Mei Yang, Jing Zhou, Haixin Li, and all other members of the Hua-Bing Li lab for discussions. This work was supported by the National Natural Science Foundation of China (82030042/32070917 to H.-B.L., 81725004 to H.L.), Shanghai Science and Technology Committee (grant no. 20JC1417400/201409005500/20JC1410100 to H.-B.L.; 20MC920200 and19XD4010000 to H.L.).

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These authors contributed equally: Baiwen Chen, Jiajia Hu, Xianting Hu.

Authors and Affiliations

ENT institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China
Baiwen Chen, Xianting Hu & Huabin Li
Shanghai Institute of Immunology, State Key Laboratory of Oncogenes and Related Genes, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
Baiwen Chen, Huifang Chen, Rujuan Bao, Yatao Zhou, Youqiong Ye & Hua-Bing Li
Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
Jiajia Hu
Zhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People’s Hospital, Zhuhai Hospital of Jinan University, Zhuhai, Guangdong, 519000, China
Meixiao Zhan
Department of General Surgery, Shanghai Minimally Invasive Surgery Center, Shanghai Institute of Immunology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Wei Cai & Hua-Bing Li
Shanghai Jiao Tong University School of Medicine - Yale Institute for Immune Metabolism, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
Hua-Bing Li

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Contributions

H.-B.L. conceived the project and designed the research. B.C., X.H., and Y.Z. designed and performed the experiments. B.C., J.H., X.H., H.C., R.B., and Y.Y. analyzed and interpreted the data. H.C. and R.B. helped with analyzing RNA-seq and ribosome profiling data. H.-B.L., H.L., W.C., Y.Y., and M.Z. discussed the projects. H.-B.L. and B.C. wrote the manuscript. This study was supervised by H.L. and H.-B.L. All authors read and approved the final manuscript.

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Correspondence to Meixiao Zhan, Wei Cai, Huabin Li or Hua-Bing Li.

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Chen, B., Hu, J., Hu, X. et al. DENR controls JAK2 translation to induce PD-L1 expression for tumor immune evasion. Nat Commun 13, 2059 (2022). https://doi.org/10.1038/s41467-022-29754-y

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Received: 31 August 2021
Accepted: 31 March 2022
Published: 19 April 2022
DOI: https://doi.org/10.1038/s41467-022-29754-y

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