Nonsense-mediated mRNA decay inhibits TRAF6-dependent anti-tumor immunity in colorectal cancer

Yanmei Wang; Zhuo Wang; Chaoqun Wang; Yan Wu; Jiaqiu Li; Lifeng Feng; Minyan Hao; Hongchuan Jin; Jia Zhou; Xian Wang

doi:10.1016/j.xcrm.2025.102463

. 2025 Nov 19;6(12):102463. doi: 10.1016/j.xcrm.2025.102463

Nonsense-mediated mRNA decay inhibits TRAF6-dependent anti-tumor immunity in colorectal cancer

Yanmei Wang ¹, Zhuo Wang ¹, Chaoqun Wang ², Yan Wu ¹, Jiaqiu Li ³, Lifeng Feng ¹, Minyan Hao ¹, Hongchuan Jin ^1,^∗, Jia Zhou ^1,^∗∗, Xian Wang ^1,^4,^∗∗∗

PMCID: PMC12765849 PMID: 41265443

Summary

Despite advances in colorectal cancer (CRC) treatment, immunotherapy shows limited efficacy due to low immunogenicity. Nonsense-mediated mRNA decay (NMD) prevents the synthesis of potentially detrimental proteins. While targeting NMD has therapeutic potential, its specific effect on CRC remains uncertain. Our research discovered significant NMD activation and upregulated SMG5 expression in CRC. Inhibition of NMD by small interfering RNA (siRNA) targeting SMG5 or NMD inhibitor NMDI14 remodeled tumor microenvironment (TME) by altering innate immune cells and enhancing CD8⁺ T cells activation. NMD inhibition also activated TBK1 through upregulation of TRAF6, which was targeted by NMD through its elongated 3′-UTR in a non-canonical manner. High SMG5 and low TRAF6 expression are associated with poor immunotherapy response. Inhibiting NMD enhanced the effectiveness of immune checkpoint blockade (ICB) therapy in CRC. By uncovering the biological relevance and translational potential of targeting NMD to reconstruct TME, this study highlights its promise as a treatment strategy for CRC.

Keywords: NMD, colorectal cancer, tumor microenvironment, TRAF6

Graphical abstract

Highlights

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NMD is significantly activated with upregulated SMG5 in CRC
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Inhibition of NMD enhances CD8⁺ T cell activation
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NMD inhibition upregulates TRAF6 to activate TBK1 axis
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NMD blockage boosts the efficacy of ICB therapy

Wang et al. demonstrate that nonsense-mediated mRNA decay (NMD) is abnormally activated in CRC. Inhibiting NMD remodels the tumor microenvironment by activating the TRAF6-TBK1 axis and boosting CD8⁺ T cell function—ultimately improving the efficacy of ICB therapy, offering a clinically actionable strategy to improve CRC immunotherapy outcomes.

Introduction

Colorectal cancer (CRC) has ranked third in terms of incidence rate and mortality among all types of cancer globally,¹ emphasizing the need for more effective and comprehensive treatment strategies.² Although immunotherapy has exhibited substantial advancements in the treatment of CRC,³ the proportion of CRC patients who can derive significant advantages from immunotherapy remains relatively limited.⁴ These patients, accounting for 10%–15%,⁵ usually have mismatch repair deficiency (dMMR), which leads to microsatellite instability (MSI). Such “hot” CRCs exhibit elevated mutation burdens and more potentiated immunogenicity.⁶ Conversely, immunotherapy only provides minimal benefits to the remaining majority of patients with microsatellite stability (MSS) or “cold” CRCs that generate fewer neoantigens.⁷ Therefore, remodeling the tumor microenvironment (TME) from “cold” to “hot” could serve as a viable therapeutic strategy for CRC.⁸

Various surveillance machineries are available to reduce the generation of neoantigens. For example, nonsense-mediated mRNA decay (NMD) is crucial for eliminating mRNAs with premature termination codons (PTCs).⁹ PTCs can promote the formation of the NMD complex comprising the up-frameshift (UPF) proteins and suppressors with morphological effects on genitalia (SMG) proteins.¹⁰ In brief, the RNA helicase UPF1 bound to the targeted RNA is phosphorylated by the phosphatidylinositol-kinase-related kinase (PIKK) SMG1. Following UPF1 phosphorylation, SMG6 acts as an endonuclease to segment the faulty mRNA,¹¹ while SMG5 and SMG7 function as adaptors to recruit the CCR4-NOT deadenylase complex and the decapping complex for the exonucleolytic degradation of target mRNAs.¹² Additionally, non-canonical NMD targets a diverse array of RNAs, such as mRNAs harboring extended 3′-UTRs or short upstream open reading frames (uORFs) in 5′-UTRs,¹³^,¹⁴ as well as small nucleolar RNAs (snoRNAs)¹⁵ and long non-coding RNAs.¹⁶ NMD dysfunction could result in the build-up of aberrant tumor-associated antigens to potentially boost anti-tumor immune responses.¹⁷ Additionally, dysfunctions in NMD may cause the generation of aberrant RNAs to activate defense immune reactions,¹⁸ such as the cGAS-STING and RIG-I-like receptor (RLR) pathways.¹⁹ Both pathways could activate TANK-binding kinase 1 (TBK1) and interferon regulatory factors to foster type I interferon (IFN-I) production.²⁰

While the role of NMD in various tumors has been reported, its function in CRC remains unclear. Here, we uncovered the evident presence of NMD signature in CRC patients. NMD inhibition in CRC, either chemically or genetically, activates TBK1 by targeting tumor-necrosis-factor-receptor-associated factor 6 (TRAF6), thereby amplifying anti-tumor immunity and mitigating cancer progression. Thus, our data indicate the translational relevance of NMD inhibition on facilitating the “cold-to-hot” transformation, promising enhanced therapeutic responses and expanded treatment paradigms for CRC and potentially other cancers.

Results

NMD inhibition represses tumor progression by altering the tumor microenvironment in CRC

In an effort to investigate the biological functions of nonsense-mediated mRNA decay (NMD) in CRC, we first conducted gene set enrichment analysis (GSEA) according to the NMD signature profile in the Cancer Genome Atlas (TCGA)-COAD dataset. The results revealed that the NMD signature was significantly enriched in CRC patients compared to adjacent normal tissues (Figure 1A), regardless of microsatellite status (Figure S1A). To further quantify NMD pathway activity at the individual level, we calculated the NMD signature scores using single-sample GSEA (ssGSEA). The resulting scores indicate that NMD activity remained consistently high across various clinical and molecular subgroups as compared to normal tissues (Figure S1B; Table S1). Therefore, we wonder whether CRC growth in vitro and in vivo would be affected after NMD inhibition by NMDI14, a specific inhibitor disrupting the interaction of UPF1 and SMG7 in the NMD pathway (Figure 1B).²¹ Elevated levels of several exemplary endogenous NMD targets,²² including BCL2-associated athanogene 1 (BAG1),²³ growth arrest-specific 5 (GAS5),²⁴ and activating transcription factor 4 (ATF4),²⁵ were indeed detected in NMDI14-treated human CRC cells, including DLD1 and RKO (representing MSI-H cell lines) and SW480 and SW620 (representing MSS cell lines) (Figures 1C and S1C). However, NMDI14 had no significant cytotoxic effect on CRC cells at 24 h, but the proliferation inhibition of CRC cells treated with NMDI14 was enhanced in a dose-dependent manner at 48 and 72 h (Figures 1D and S1D). Consistently, the growth of MC38 (MSI-type murine colon cancer) cells on nude mice and C57BL/6 mice was compromised, while no significant difference for mice body weight was found between the NMDI14-treated group and the control group (Figures S2A and S2B).

Notably, the growth inhibitory effects of NMDI14 were much more pronounced in the C57BL/6 mice than in nude mice (Figures 1E–1J). In light of the immunocompromised state of nude mice, we postulated the involvement of immune response in the anti-tumor effect of NMD inhibition and employed flow cytometry to characterize the composition of immune cell populations infiltrating the TME. In nude mice, NMDI14 increased the infiltration of dendritic cells (DCs), natural killer (NK) cells, and neutrophils in tumor-bearing nude mice, while the relative abundance of macrophages showed slight decrease (Figures S2C–S2F). In tumor-bearing C57BL/6J mice, NMDI14 increased the infiltration of macrophages, DCs, and NK cells, while the relative abundance of neutrophils showed minimal fluctuations (Figures S2G–S2J). In addition to these innate immune-related cell populations, cytotoxic T cells (CD8⁺) (Figure 1K), especially IFNγ⁺ CD8⁺ T cells (Figure 1L), were significantly increased, along with increased serum and intracellular levels of interferon gamma (IFN-γ) (Figure 1M), which plays a prominent role in eliminating solid tumors as a potent cytokine produced by immune cells.²⁶ Immune profiling was also performed in the BALB/c mice model bearing CT26 (MSS-type murine colon cancer). Immunohistochemistry (IHC) revealed an increased infiltration of multiple innate immune cell populations in CT26 tumors following NMDI14 treatment—including macrophages, NK cells, dendritic cells, and neutrophils (Figure S3A). Triple immunofluorescence (IF) staining also revealed a notable increase in CD8⁺ T cell density (Figure S3B). Therefore, NMDI14 might exert its anti-tumor effect by activating the innate and adaptive immune response. Consequently, we further established a tumor xenograft model using NOD/SCID immunodeficient mice. No significant difference in tumor growth was observed with or without NMDI14 treatment (Figure S3C), confirming that the anti-tumor effect of NMDI14 relied on the activation of the host immune system, rather than the cytotoxic effects of the compound itself. Collectively, these results suggested that inhibiting NMD in tumors enhances anti-tumor immunity probably by promoting a shift from “cold” tumor environment to “hot” environment.

SMG5 deficiency inhibits tumor growth and enhances anti-tumor immunity in CRC

To examine the key regulatory genes related to anti-tumor immunity within the NMD system, we divided CRC patients from the TCGA-COAD dataset into “hot” and “cold” tumor types based on the level of CD8⁺ T cell infiltration in the tumor. We found that SMG5, a member of the suppressors with morphologic effects on genitalia (SMG) protein family, was enriched in cold tumors and exhibited the largest fold change among 10 NMD candidates (false discovery rate [FDR] <0.05, |log₂FC| = 4.15) (Figure 2A; Table S2). Next, we conducted differential analysis to profile genes differentially expressed on tumor cells with high and low SMG5 expression (Figure 2B). These differentially expressed genes were found to be highly enriched in immune-related pathways, including immune system process, leukocyte activation, and so on (Figure 2C). In line with this, GSEA comparing SMG5-high with SMG5-low tumor cells revealed a selective enrichment for signatures related to immune regulation such as IFN-γ response in the SMG5-low group (Figure 2D). Taking the results in Figure 1 together, we chose SMG5 as a key candidate to uncover the relevance of NMD to anti-tumor immunity in CRC.

Therefore, we first analyzed SMG5 expression in CRC tissues and para-cancerous colon tissues. The protein levels of SMG5 were significantly elevated in CRC tissues compared to non-tumor tissues (Figures 2E, S4A, and S4B). What’s more, its expression in most CRC cell lines was upregulated when compared to normal human colonic epithelial cells (NCM460) (Figure S4C). High SMG5 expression was associated with poorer survival in CRC (particularly in MSI patients), suggesting its potential role in compromising anti-tumor immunity (Figure S4D). Furthermore, the mRNA level of SMG5 also showed a gradual increase with the progression of the disease, highlighting its relevance to CRC pathogenesis (Figure S4E). SMG5 expression correlated negatively with tumor mutational burden (TMB), suggesting that elevated NMD activity may reduce tumor immunogenicity and facilitate immune evasion (Figure S4F). Higher SMG5 expression was associated with increased TIDE scores, indicating a potential link to reduced immune checkpoint blockade (ICB) efficacy and poorer clinical outcomes (Figure S4G). Additionally, high SMG5 expression was associated with reduced CD8⁺ T cell infiltration and limited upregulation of immune-related factors and checkpoints, indicating a suppressed, immunologically “cold” TME (Figures S4H and S4I).

Subsequently, we knocked down the SMG5 expression in CRC cells by small interfering RNAs (siRNAs) (Figure S5A). Through RT-qPCR and CCK-8, it was confirmed that the SMG5 knockdown suppressed NMD activity(Figures 2F and S5B) and inhibited the proliferation of colon cancer cells in vitro (Figures 2G and S5C), consistent with the effect of NMDI14. In addition, MC38 cell lines stably expressing Smg5-specific shRNA or scrambled control short hairpin RNA (shRNA) were constructed (Figure S5D) to establish a subcutaneous xenograft tumor model in the C57BL/6J mice. Similar to the results of the NMDI14 treatment, Smg5 knockdown suppressed tumor growth in vivo (Figure S5E). In order to better understand the role of SMG5 in intestinal immune response in vivo, we engineered mice with an intestinal epithelial cell (IEC)-specific knockout of Smg5, termed Smg5^ΔIEC mice, with Smg5^flox/flox mice serving as the control group (Figures S5F–S5H). Next, we subjected the Smg5^flox/flox and Smg5^ΔIEC mice to a specific AOM/DSS regimen well used to induce CRC in mice (Figure S5I).²⁷ As anticipated, AOM/DSS induced tumors distributed from the distal end to the medial part of the colon (Figure 2H). Strikingly, the colorectum in Smg5^ΔIEC mice had fewer and smaller tumors (2.38% versus 19.65% for >4 mm tumors) than in Smg5^fl/fl mice (Figures 2I–2K). More CD11c⁺ DCs and Ly6G⁺ neutrophils within the intestinal cancer tissues were observed in Smg5^ΔIEC mice than Smg5^fl/fl mice (Figure S5J), accompanied by the significant increase of CD4⁺ T cells and CD8⁺ T cells in intestinal cancer tissues from Smg5^ΔIEC mice (Figure 2L). As a whole, SMG5 deficiency inhibits tumor growth and enhances anti-tumor immunity in CRC.

TBK1 signaling is activated by NMD suppression

The GSEA result revealed that SMG5-deficiency-mediated immune response might be related to the activation of IFN signaling (Figure 3A). The mRNA levels of inflammatory cytokines and IFN-β were significantly elevated following NMDI14 treatment or SMG5 knockdown in RKO cells (Figures 3B and 3C). The IFN-α/β receptor (IFNAR) is a heterodimeric signaling receptor composed of IFNAR1 and IFNAR2, both of which are essential for IFN-I signaling.²⁸^,²⁹ To determine whether the anti-tumor effect of NMDI14 depends on IFN-I signaling, we administered a monoclonal antibody that specifically blocks IFNAR1 in mice. In tumor-bearing mice, the anti-IFNAR monoclonal antibody significantly reversed the anti-tumor effects of NMDI14 and suppressed the infiltration of innate immune cells and CD8⁺ T cells (Figures 3D–3G), indicating that inhibiting NMD activates anti-tumor immunity through the IFN-I signaling pathway.

NMDI14 or SMG5 deficiency triggered the nuclear translocation of IRF3 (Figures S6A and S6B), which is the well-known key step for the production of IFN-I and proinflammatory cytokines.³⁰ However, IRF3 nuclear translocation induced by NMDI14 treatment or SMG5 knockdown was significantly inhibited by knockdown of TBK1 (Figures 3H and 3I), a key upstream kinase responsible for IRF3 phosphorylation and activation.³¹^,³² Additionally, the phosphorylation of TBK1 was significantly increased after NMDI14 treatment or SMG5 knockdown (Figures 3J and 3K and S6C–S6I), accompanied by the dimerization of TBK1, a process required for its efficient phosphorylation (Figure S6J). Moreover, the phosphorylation levels of TBK1 in subcutaneous tumors were also significantly elevated after NMDI14 treatment (Figure 3L). In summary, we found that NMD inhibition activates TBK1 signaling.

TRAF6 is required for TBK1 activation resulting from NMD inhibition

Next, we sought to determine the specific mechanism by which the inhibition of NMD activated the phosphorylation of TBK1 in CRC. Both the cGAS-STING signaling pathway, which recognizes cytosolic DNA, and the RLRs-MAVS signaling pathway, which detects cytosolic abnormal RNAs, are linked to TBK1 activation.¹⁹ However, neither cGAS-STING nor RIG-I/MDA-5-MAVS-TRAF3 factors seems to affect NMDI14-induced activation of TBK1 signaling (Figures S7A–S7F). Tumor-necrosis-factor-receptor-associated factors (TRAFs) are intracellular adaptor proteins involved in signal transduction. Among them, TRAF3 has been shown to interact with TBK1 through an IProx-like motif, which is a conserved short sequence characterized by a Pro-Xaa-Glu/Asp (P-X-E/D) pattern and facilitates TBK1 dimerization and phosphorylation.³³ NMD inhibition simultaneously induced the activation of the nuclear factor κB (NF-κB) pathway (Figure 4A), which could be activated by TRAF6, a member of the TRAF family with structural similarities to TRAF3,³⁴ functioning as a classical E3 ubiquitin ligase. Interestingly, TRAF6 mRNA levels were reduced in CRC tissues when compared with non-cancerous tissues, regardless of microsatellite status (Figures 4B and S7G). According to the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Human Protein Atlas (HPA) databases, TRAF6 protein expression was also decreased in CRC tissues (Figures 4C and S7H). Higher TRAF6 expression was associated with better survival in CRC patients, suggesting its potential tumor-suppressive role (Figure S7I). Notably, we discovered that TRAF6 is a high-confidence TBK1 interactor as predicted by STRING. It stands out due to its consistent expression pattern and prognostic relevance in colorectal cancer, highlighting its potential functional relevance (Figures S8A–S8E). Additionally, immune scoring analysis revealed a positive correlation between TRAF6 mRNA expression and the immune scores of T cells (CD4⁺/CD8⁺), neutrophils, macrophages, and dendritic cells (Figure S9A). High TRAF6 expression was consistently associated with increased infiltration of multiple immune cell types and elevated expression of immune checkpoint genes, indicative of an immunologically “hot” and active TME (Figures S9B and S9C). We further used the CIBERSORT algorithm to assess immune cell infiltration patterns across the MSS and MSI subgroups in TCGA-COAD. As expected, the overall correlation between gene expression and immune infiltration was weaker in MSS tumors than in MSI tumors. SMG5 and TRAF6 exhibited opposing correlations with innate immune cell infiltration in both MSI and MSS subtypes, suggesting that they play distinct roles in modulating the tumor immune microenvironment (Figure S9D). Therefore, we assumed that NMD inhibition might activate TBK1 through TRAF6 to modulate tumor immune response.

In fact, NMDI14 or SMG5 deficiency could no longer effectively activate TBK1 once TRAF6 was knocked down (Figures 4D and 4E). Meanwhile, NMDI14-induced increase in mRNA levels of inflammatory cytokines was also compromised upon TRAF6 knockdown (Figure 4F). Moreover, TRAF6 inhibitor C25-140 succeeded to reverse NMD-inhibition-triggered TBK1 phosphorylation and IRF3 nuclear translocation (Figures 4G and 4H). As a result, C25-140 antagonized the anti-tumor effect of NMDI14 in vivo (Figure 4I) and rescued microenvironment changes induced by NMDI14 (Figures 4J and S9E). To sum up, TRAF6 is required for the activation of TBK1 signaling resulting from NMD inhibition.

TRAF6 interacts with TBK1 to facilitate its Lys63-linked polyubiquitination of TBK1

We further endeavored to provide more insight into the regulatory function of TRAF6 in the activation of TBK1. As expected, the overexpression of TRAF6 increased the phosphorylation of TBK1 (Figure S10A), while the knockdown of TRAF6 reduced TBK1 phosphorylation (Figure S10B). TRAF6 inhibitor C25-140 effectively inhibited the NMDI14-induced polyubiquitination and subsequent phosphorylation of endogenous TBK1 (Figure 5A). Conversely, the catalytic-inactive C70A mutant of TRAF6 (TRAF6-C70A) failed to induce the polyubiquitination and phosphorylation of TBK1 (Figure 5B). The dual-luciferase reporter assay also demonstrated that the overexpression of TRAF6, but not TRAF6-C70A, could amplify the activity of the IFN-β promoter (Figure 5C). These results substantiated that TBK1 phosphorylation depends on the E3 ligase activity of TRAF6.

The ubiquitin contains seven lysine residues (Lys6, Lys11, Lys27, Lys29, Lys33, Lys48, and Lys63), resulting in seven corresponding polyubiquitination modifications.³⁵ Among them, Lys48-linked (K48) and Lys63-linked (K63) polyubiquitination have been studied extensively. Therefore, we initially cotransfected plasmids expressing TRAF6 and wild-type Ub or Ub mutants (K48R or K63R). It seems that TRAF6 mediated K63 but not K48-linked polyubiquitination of TBK1 (Figure 5D). Furthermore, none of the other Ub mutants (K6R, K11R, K27R, K29R, or K33R) were capable of influencing TRAF6-mediated polyubiquitination of TBK1 (Figure 5E), thus verifying the relevance of TRAF6 to K63-linked polyubiquitination of TBK1. Consistently, K63-linked polyubiquitination of TBK1 was confirmed after the overexpression of TRAF6 using the antibody specific to K63-linked polyubiquitin (Figure 5F). Moreover, the interaction of TBK1 and TRAF6 was evidenced by co-immunoprecipitation (coIP) and co-localization experiments, and the binding capacity was further enhanced by NMDI14 (Figures 5G, S10C, and S10D).

Next, various truncations of TBK1 and TRAF6 were generated to further explore the respective domains responsible for their interaction. Generally, the functional domains of TRAF6, such as the ring finger domain (RF), zinc-finger domain (ZF), and coiled-coil domain (CC), play crucial roles in its activities and interaction with other proteins. After the co-expression of Myc-tagged wild-type TBK1 with Flag-tagged wild-type TRAF6 or TRAF6 truncation mutants (Figure 5H), we found that the TRAF-C domain in the TRAF6 protein was a fundamental domain that directly interacted with the TBK1 protein (Figure 5I). In the absence of the RF/ZF/CC in TRAF6, the TBK1 protein could still be bound, albeit with reduced stability and lower expression levels of the truncated TRAF6-C protein. We further constructed a TRAF6-C domain deletion mutant (ΔTRAF6-C). CoIP results showed that ΔTRAF6-C could not capture TBK1 protein nor could TBK1 precipitate ΔTRAF6-C protein, confirming that the TRAF6-C domain was indeed the key structural domain for direct interaction with TBK1 protein (Figure S10E). However, deleting only the RF domain resulted in the loss of interaction with TBK1. This loss may be due to the exposed ZF domain in the ΔRF truncation, masking the key domain on TRAF6-C and thus preventing the interaction with TBK1 protein. To test this hypothesis, we constructed a TRAF6-ZF truncation. The coIP results demonstrated that the presence of TRAF6-ZF indeed significantly hindered the interaction between wild-type TBK1 and TRAF6 protein (Figure S10F).

On the other hand, TBK1 includes four domains, specifically the N-terminal kinase domain (KD), a ubiquitin-like domain (ULD), and two CC domains.³⁶ Similarly, TBK1 truncation mutants were co-immunoprecipitated with wild-type TRAF6 (Figure 5H), and the results confirmed that the KD or ULD region was indispensable for the interaction of TBK1 with TRAF6 (Figure 5J). Docking analysis using the HDOCK server was conducted to further confirm their interaction mode. The results indicated that the binding energy between TBK1 and TRAF6 was −254.64 kcal/mol, suggesting a strong binding affinity between them. Additionally, the analysis revealed interactions between the TRAF6-C domain and TBK1’s KD and ULD domains (Figure S10G), corroborating our previous coIP results. Furthermore, as residues surrounding the protein-protein interaction interface can form hydrogen bonds, these non-covalent bonds contribute to the stabilization of the protein complex. In a detailed study of the interactions between TRAF6-C and TBK1, we found that Glu-301, Asp-296, Gln-368, His-64, and Glu-128 of TBK1 form hydrogen bonds with Arg-466, Arg-392, Asn-359, Glu-370, and Gln-396 of TRAF6-C, respectively. These hydrogen bonds, ranging from 2.1 Å to 2.9 Å in length, are crucial for maintaining the stability of the protein complex (Figure S10H). In summary, NMD inhibition enhances the interaction of TRAF6 with TBK1 to facilitate Lys63-linked polyubiquitination of TBK1.

TRAF6 mRNA is a non-canonical NMD target

Subsequently, we delved deeper into the regulation of TRAF6 following NMD inhibition. The protein expression of TRAF6 increased over time under treatment with NMDI14 or after SMG5 knockdown (Figures 6A, 6B, S11A, and S11B). The mRNA levels of TRAF6 also increased under the indicated treatments (Figures 6C, 6D, S11C, and S11D), suggesting that NMD inhibition predominantly affected the transcriptional or post-transcriptional level of TRAF6. In fact, the mRNA level of TRAF6 was relatively higher in patients with low SMG5 expression compared to those with high levels of SMG5 (log₂FC = 0.924, FDR <0.05) (Figure 2B). Furthermore, the RNA stability assay indicated that NMDI14 treatment or SMG5 deficiency extended the half-life of TRAF6 mRNA (Figures 6E, 6F, S11E, and S11F). In summary, NMD inhibition stabilized TRAF6 mRNA to increase TRAF6 protein.

We hypothesized that the TRAF6 mRNA might be subject to NMD due to its long 3′-UTR. Since non-classical NMD relies on the binding of UPF1 to the mRNA, particularly within elongated 3′-UTRs,³⁷ we first predicted the binding site of UPF1 protein on the 3′-UTR of TRAF6 mRNA using the online tool StarBase v3.0 (Figure 6G). The gene expression correlation scatterplot also illustrated the negative correlation between UPF1 and TRAF6 in CRC (Figure 6H). The RNA immunoprecipitation (RIP) assay further confirmed that UPF1 protein could enrich TRAF6 mRNA, and the enrichment was further increased after NMDI14 treatment (Figure 6I).

Next, the de novo motif analysis was completed to evaluate the region targeted by UPF1 protein in the 3′-UTR of TRAF6. While observing the addressed motifs from StarBase v3.0, a sequence “CUGUU” was noted with the smallest p value, which had 43.45% predicted targets and 38.97% backgrounds and then matched in the 3′-UTR of TRAF6 (Figure 6J, left panel). Additionally, a previous report put forward that GC-rich motifs, which contained the CUG sequence, are markedly identified in the 3′-UTRs of UPF1 target mRNAs (Figure 6J, right panel).³⁸ Interestingly, both motifs were present in the 3′-UTR of TRAF6 mRNA with their initiation-end sites as 639th–643rd and 730th–736th, respectively, from the start site of the 3′-UTR (Figure 6J). Therefore, we individually generated two biotin-labeled RNA probes with the putative binding sites to conduct RNA pull-down assays in CRC cells. The findings confirmed that both wild-type probes could pull down the endogenous UPF1 protein (Figure 6K). However, the second mutant probe failed to pull down the UPF1 protein, while the first mutant probe could continue to interact with the UPF1 protein (Figure 6L), indicating that the probe with the first motif may contain a secondary structure capable of binding to the UPF1 protein. Therefore, we concluded that TRAF6 is the target mRNA of NMD, and NMD inhibition can upregulate TRAF6 levels by stabilizing its mRNA.

NMDI14 inhibition confers sensitivity to ICB therapy in CRC

Immunotherapy has significantly advanced cancer treatment, with the success of programmed cell death protein 1 (PD-1) blockade in many malignancies. We also found that low SMG5 mRNA levels were correlated with improved overall survival (OS) in cancer patients treated with ICB therapy, including Nivolumab (anti-PD-1) or Ipilimumab (anti-CTLA-4) (Figure 7A). Additionally, in patients treated with ICB therapy, TRAF6 mRNA levels were significantly positively correlated with OS or progression-free survival (PFS), signifying that patients with tumors that displayed low SMG5 or high TRAF6 expression could yield a better response to ICB (Figure 7B). It has been reported that the secretion of IFN-γ by T cells not only promotes T cell activity but also induces T cell exhaustion and adaptive immune resistance by upregulating the inhibitory PD-1/PD-L1 signaling,³⁹ necessitating the combination of ICB therapy with STING/TBK1 activation. Indeed, the proportion of PD-1⁺ CD8⁺ T cells in NMDI14-treated or Smg5-knockdown tumors was significantly increased (Figures 7C and 7D). Notably, the combination of NMDI14 and anti-mouse PD-1 antibody realized much better responses with regard to inhibiting the growth of subcutaneous tumors than NMDI14 alone (Figure 7E). In an additional immune-competent BALB/c CRC model, we also validated that the combination of NMDI14 and anti-PD-1 treatment enhanced the efficacy of ICB therapy (Figure 7F). Since transforming growth factor β (TGF-β) acts as a negative regulator of anti-tumor immunity, dual targeting of TGF-β and the PD-1/PD-L1 axis has shown improved anti-tumor responses and prolonged survival in preclinical models.⁴⁰^,⁴¹ In our study, we combined the TGF-β receptor type I (TGF-βRI) kinase inhibitor, Galunisertib, with the anti-PD-1 antibody as a positive control. We found that NMDI14 combined with anti-PD-1 treatment produced almost comparable anti-tumor efficacy, further highlighting the potential for clinical application of this combinatorial strategy (Figure 7F). In conclusion, NMD inhibition confers sensitivity to ICB therapy in CRC.

NMD inhibition confers sensitivity to ICB therapy in CRC

(A) The correlation of SMG5 mRNA levels with the overall survival (OS) of patients with melanoma who were receiving Nivolumab (anti-PD-1) or Ipilimumab (anti-CTLA-4).

(B) The correlation of TRAF6 mRNA levels with the OS of male patients with melanoma receiving Ipilimumab, as well as progression-free survival (PFS) of patients with melanoma receiving Pembrolizumab (anti-PD-1).

(C and D) Representative flow cytometric analysis images (left) and relative quantification (right) of infiltrating PD-1⁺ CD8⁺ T cells within tumor tissues with indicated treatments. n = 4 mice per group. The data are presented as means ± SD; ∗p < 0.05; ∗∗p < 0.01.

(E) Mice weights curves, tumor growth curves, tumor weights, and the representative tumor image of subcutaneous xenografts in male C57BL/6J mice with corresponding treatments. n = 6 mice per group. The data are presented as means ± SD; ∗p < 0.05; ∗∗p < 0.01.

(F) Tumor growth curves, tumor weights, and the representative tumor image of subcutaneous xenografts in BALB/c mice with corresponding treatments. n = 6 mice per group. The data are presented as means ± SD; ∗p < 0.05; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001; “ns” indicates no significance.

(G) Hypothetical model of NMD inhibition function in CRC (Drawn by BioRender; Agreement number: EQ28OJONEI). (1) TRAF6 is targeted by NMD through its elongated 3′-UTR. (2) TRAF6 mediates Lys63-linked polyubiquitination of TBK1 essential to its phosphorylation-dependent activation. (3) The TBK1-IRF3-IFN-I signaling pathway is activated, remodeling TME. (4) NMD inhibition confers sensitivity of CRC to ICB therapy.

Discussion

The clinical responses of immunotherapy in the majority of patients with CRC remain weak, presenting a significant therapeutic challenge. TME characterization, specifically designated as cold or hot, has gained attention due to its implications on tumor immunity and therapeutic responsiveness. In this study, we observed that NMD inhibition demonstrated great anti-tumor immune effects by remodeling cold CRC tumors to a hot phenotype. This transformation was attributed to the activation of TBK1-dependent innate immune response following the accumulation of TRAF6 protein. We not only identified TRAF6 as a previously unrecognized NMD target but also found it mediated the Lys63-linked polyubiquitination of TBK1 essential for its phosphorylation-dependent activation (Figure 7G). These results indicate that NMD inhibition presents a promising approach to potentiate anti-tumor immune response in cancer patients previously considered unresponsive to immune therapy, and SMG5 can serve as a target in the drug design for the potential precision treatment of CRC and potentially other cancers like melanoma.

When genetic mutations introduce a PTC into an essential gene, the resulting loss or alteration of the protein’s function due to the presence of NMD can lead to various genetic disorders. For example, cystic fibrosis is attributed to mutations in the CFTR gene, and the utilization of an NMD inhibitor, NMDI14, has been observed to stabilize CFTR mRNAs containing PTCs, potentially enabling the translation of functional, albeit truncated, CFTR proteins and alleviating disease symptoms.⁴² In addition, many acquired mutations in cancer cells will generate PTCs in p53 and other cancer genes,⁴³ and the combination of readthrough drug G418 and NMDI14 enabled the effective expression of the full-length p53.²¹ The safety of NMDI14 has been tested, and it has been found that the pharmacologic inhibition was achieved with limited toxicity or adverse effects.²¹ In fact, our results proved that the TBK1 signaling was significantly activated by NMDI14 that did not compromise cell viability dramatically and demonstrated excellent safety even during prolonged administration in subcutaneous xenograft tumor models (Figure S1A). Given the concerns about the potential side effects resulting from NMD inhibition, our several ongoing projects are exploring the broader biological impact of NMD inhibition, and preliminary findings suggest that SMG5 knockdown or NMDI14 exerts selective and context-dependent effects, including potential roles in mitochondrial quality control and metabolic regulation. Nevertheless, the results of our animal studies and other groups as mentioned above indicate that NMD inhibition is well tolerated at the dosage required to induce strong anti-tumor effects.

Exploring and identifying target molecules is a key component of precision cancer treatment. We also intended to identify specific genes to be targeted rather than inhibiting the overall NMD. UPF1 is renowned for its role in the NMD pathway, and even inhibition of UPF1 is used to inhibit NMD.⁴⁴ However, beyond NMD, UPF1 is also required to maintain genome stability by participating in DNA damage repair process, thereby mitigating the accumulation of mutations and curbing oncogenic transformations.⁴⁵ Moreover, UPF1 can function as a tumor suppressor to facilitate the activation of pro-apoptotic pathways in gastric cancer and lung adenocarcinoma.⁴⁶^,⁴⁷ Conversely, UPF1 can also bolster cell proliferation, promoting tumor growth in endometrial cancer and glioblastoma.⁴⁸^,⁴⁹ In addition, UPF1 also directly downregulated HFE mRNA encoding a major histocompatibility complex (MHC) class I molecule to compromise effective antigen presentation.⁵⁰ Therefore, UPF1 is not a suitable target to inhibit NMD for cancer treatment. In this study, bioinformatics analysis of cold and hot CRC provided us with a better targeting option, SMG5, which is involved in NMD as the executor of RNA degradation.⁵¹ Furthermore, the immune activation observed after the knockdown of SMG5, both in vivo and in vitro, highlights its promising therapeutic potential. Additionally, the induction of CRC in transgenic Smg5^ΔIEC mice using AOM/DSS regimen, which resulted in histological manifestations resembling sporadic human colon cancer, was significantly alleviated, accompanied by increased infiltration of innate immune cells and cytotoxic CD8⁺ T cells in tumor tissues, further supporting the notion of SMG5 as a potential therapeutic target in CRC. Given the need for precise patient selection, we propose that NMD-targeted therapy may be most suitable for tumors with SMG5 overexpression. With the successful application of siRNA-based drugs recently, siRNAs targeting SMG5, which was upregulated in cancer cells, hold promise for the potential development of siRNA-based drugs targeting NMD to improve efficacy of immunotherapy for cancer patients.

TRAF6 functions as a crucial adapter protein that coordinates various inflammatory responses. Upon ligand binding, pattern recognition receptors including Toll-like receptors (TLRs) and IL-1R promote TRAF6 oligomerization and activation to stimulate the activation of NF-κB signaling.⁵² In the presence of lipopolysaccharides (LPSs), TRAF6 interacts with TBK1 to facilitate the phosphorylation of STAT3 by TBK1.⁵³ However, the specific mechanism underlying the connection between TRAF6 and TBK1 remains unclear. In this study, we demonstrate that TRAF6 can directly facilitate Lys63-linked polyubiquitination of TBK1, thereby promoting its phosphorylation. Additionally, we observed an enhanced interaction between TRAF6 and TBK1 following inhibition of NMD, primarily attributed to an elevation in the protein expression of TRAF6 after the inhibition of NMD. Therefore, the regulation of TRAF6 abundance rather than its post-translational-modification-dependent activation in response to acute infection or environmental stimuli may play an important role in the chronic process of tumorigenesis.

Although TRAF6 is unlikely to be the only factor affected by NMD, we have demonstrated that TRAF6 is necessary for NMD-inhibition-induced TBK1 activation. We discovered that TRAF6 served as a target for NMD due to the presence of a lengthy 3′-UTR, which is recognized and bound by UPF1. Although we found the specific binding region between TRAF6 3′-UTR and UPF1 as predicted,³⁸ we failed to validate the motif predicted by StarBase v3.0, since both wild-type and mutant RNA probes still maintained their interaction with UPF1. However, mRNAs with elongated 3′-UTRs are susceptible to NMD, potentially due to the formation of unique secondary structures from base pairing within the RNA strand³⁷ or the binding of specific proteins that recognize the mRNA as aberrant. The helicase function of UPF1 facilitates the unwinding of secondary structures, enabling its binding and movement along the RNA.⁵⁴ Therefore, the binding capacity of UPF1 might be intricately linked to the secondary or tertiary structure of RNA probes, so a reduction in site mutations does not impede its binding capability to UPF1. Additionally, it is plausible that other proteins may also engage with the 3′-UTR for the subsequent NMD. Nevertheless, these reflections expand the potential avenues for future investigations and need further explorations in identifying novel NMD substrates and targeting NMD for the treatment of human cancers.

Interestingly, in addition to recruiting immune cells to the TME, NMD inhibition may activate the protective response by inducing T cell exhaustion, as chronic IFN-I signaling maintains high levels of PD-1 expression to induce the depletion and dysfunction of T cells.⁵⁵ Additionally, it has been reported that in melanoma, TRAF6 promotes the formation of the YAP1/TFCP2 transcriptional complex and PD-L1 transcription.⁵⁶ Being immune checkpoints, PD-1/PD-L1 are important to protect tumor cells from the attacks of immune cells like cytotoxic CD8⁺ T cells, thus facilitating immune escape and tumor development. Fortunately, such a protection could be disrupted by ICB therapy. As a result, NMD inhibition not only recruited more immune cells to switch “cold” tumors into “hot” tumors but also conferred sensitivity to ICB therapy.

In addition, our data demonstrate that NMD hyperactivation is a shared feature of CRC, irrespective of MSI status or adenomatous polyposis coli (APC) mutations. The effects of NMD inhibition—TRAF6 stabilization, TBK1 phosphorylation, and anti-proliferative responses—were consistently observed in both the MSI-H (RKO/DLD1) and MSS (SW480/SW620) models, suggesting that targeting this axis may bypass the limitations posed by common driver alterations.

In conclusion, NMD is important to promote the pathogenesis of CRC. SMG5 emerges as a promising and specific therapeutic target for inhibiting NMD in CRC. TRAF6 was identified as a non-canonical NMD substrate for inhibiting innate immune response in CRC. Genetic depletion of SMG5 or chemical inhibition of NMD effectively activates TBK1 to compromise CRC development and confers immunotherapy sensitivity, representing a potential strategy to improve the clinical efficacy of immune therapy in CRC.

Limitations of the study

While this study establishes the critical role of the TRAF6-TBK1 signaling axis in NMD-inhibition-mediated TME remodeling, it has notable limitations that warrant future investigations. Although we validated the importance of the TRAF6-TBK1 pathway, we did not characterize the global transcriptomic changes induced by NMD inhibition. When NMD is suppressed, these PTC-containing mRNAs (and potentially other NMD targets) evade degradation and undergo translation. However, we did not resolve the full spectrum of such transcriptomic alterations nor did we investigate whether short peptides translated from these rescued mRNAs could function as tumor antigens to further boost anti-tumor immunity. Addressing these questions is critical to fully understand the broad immunomodulatory effects of NMD inhibition and would strengthen its rationale as a CRC therapeutic target.

Resource availability

Lead contact

For additional details, as well as requests related to resources and reagents, please reach out to the lead contact, Xian Wang (wangx118@zju.edu.cn).

Materials availability

No novel, unique reagents were developed as part of this research. However, the plasmids generated in this study can be obtained from the lead contact upon a completion of a materials transfer agreement.

Data and code availability

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The datasets TCGA-COAD and corresponding clinical patient information analyzed for this study can be found in GDC: TCGA-COAD (accessed on 9 November 2021). All data relevant to the experiments are included in the supplemental information. The software and algorithms employed for data analysis in this study have been previously published, with relevant citations included throughout the STAR Methods section.
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This study does not present any original code.
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Any additional information necessary to reanalyze the data reported in this study is available from the lead contact (Xian Wang, wangx118@zju.edu.cn) upon request.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (82373310 and 82473486) and the Natural Science Foundation of Zhejiang Province, China (LD22H160003).

Author contributions

X.W., J.Z., and H.J. conceived and supervised the study. X.W., J.Z., H.J., and L.F. designed the work. Y. Wang. and Y. Wu. performed the experiments and acquired data. Z.W. and C.W. provided a pathological examination and performed clinical analysis. J.L. and M.H. analyzed bioinformatics data. Y. Wang., Z.W. and J.L. drew the figures. X.W. and J.Z. provided funding for the study. Y. Wang. crafted the manuscript. X.W., J.Z., H.J., and Z.W. revised the manuscript. All authors have read and agreed to the final version of the manuscript.

Declaration of interests

The authors declare no competing interests.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies

Anti-β-Actin	ABclonal	Cat# AC026; RRID:AB_2768234
Anti-SMG5	Abcam	Cat# ab33033; RRID:AB_882612
Anti-IRF3	Abcam	Cat# ab68481; RRID:AB_11155653
Anti-LAMB1	Proteintech	Cat# 12987-1-AP; RRID:AB_2136290
Anti-GAPDH	Epitomics	Cat# 2251-1
Anti-p-TBK1	Cell Signaling Technology	Cat# 5483; RRID:AB_10693472
Anti-TBK1	Cell Signaling Technology	Cat# 3504; RRID:AB_2255663
Anti-p-IRF3	Abcam	Cat# ab76493; RRID:AB_1523836
Anti-cGAS	Cell Signaling Technology	Cat# 15102; RRID:AB_2732795
Anti-STING	Cell Signaling Technology	Cat# 50494; RRID:AB_2799375
Anti-RIG-I	Cell Signaling Technology	Cat# 3743; RRID:AB_2269233
Anti-MDA-5	Cell Signaling Technology	Cat# 5321; RRID:AB_10694490
Anti-TRAF3	ABclonal	Cat# A3094; RRID:AB_2764895
Anti-TRAF6	Abcam	Cat# ab40675; RRID:AB_778573
Anti-Flag-tag	Sigma-Aldrich	Cat# F1804-1
Anti-Myc-tag	Cell Signaling Technology	Cat# 2276; RRID:AB_331783
Anti-Ubiquitin	Cell Signaling Technology	Cat# 58395; RRID:AB_3075532
Anti-Ubiquitin (K63)	Abcam	Cat# ab179434; RRID:AB_2895239
Anti-UPF1	Cell Signaling Technology	Cat# 9435; RRID:AB_10629662
Anti-F4/80	Cell Signaling Technology	Cat# 70076; RRID:AB_2799771
Anti-CD11c	Cell Signaling Technology	Cat# 97585; RRID:AB_2800282
Anti-LY6G	Abcam	Cat# ab238132; RRID:AB_2923218
Anti-NCR1	Abcam	Cat# ab233558; RRID:AB_2904203
Anti-CD4	Abcam	Cat# ab183685; RRID:AB_2686917
Anti-CD3	Abcam	Cat# ab237721; RRID:AB_3662950
Anti-CD8	Abcam	Cat# ab209775; RRID:AB_2860566
Anti-IFNAR monoclonal antibody	Bioxcell	Cat# BE0241
Goat anti-Mouse IgG (H + L) HRP	Thermo Fisher scientific	Cat# 62-6520; RRID:AB_2533947
Goat anti-rabbit IgG (H + L) HRP	Thermo Fisher scientific	Cat# 62-6120
Alexa Fluor™ 488 goat anti-rabbit IgG (H + L) secondary antibody	Thermo Fisher scientific	Cat# A-11034; RRID:AB_2576217
Alexa Fluor™ 568 goat anti-mouse IgG (H + L) secondary antibody	Thermo Fisher scientific	Cat# A-11004; RRID:AB_2534072
Anti-mouse CD45 (Pacific Blue) (30-F11)	Biolegend	Cat# 103126; RRID:AB_493535
Anti-mouse CD3 (APC) (17A2)	Biolegend	Cat# 100236; RRID:AB_2561456
Anti-mouse CD4 (APC/Cyanine7) (GK1.5)	Biolegend	Cat# 100414; RRID:AB_312699
Anti-mouse CD8a (PE) (53–6.7)	Biolegend	Cat# 100707; RRID:AB_312746
Anti-mouse CD8a (APC) (53–6.7)	Biolegend	Cat# 100712; RRID:AB_312751
Anti-mouse CD11c (PE) (N418)	Biolegend	Cat# 117307; RRID:AB_313776
Anti-mouse MHC-II (APC/Cyanine7) (M5/114.15.2)	Biolegend	Cat# 107627; RRID:AB_1659252
Anti-mouse F4/80 (APC/Cyanine7) (BM8)	Biolegend	Cat# 123118; RRID:AB_893477
Anti-mouse Gr-1 (APC/Cyanine7) (RB6-8C5)	Biolegend	Cat# 108424; RRID:AB_2137485
Anti-mouse NK1.1 (PE) (S17016D)	Biolegend	Cat# 156504; RRID:AB_2783136
Anti-mouse Ly6G (Brilliant Violet 650) (1A8)	Biolegend	Cat# 127641; RRID:AB_2565881
Anti-mouse PD1 (PE) (29F.1A12)	Biolegend	Cat# 135206; RRID:AB_1877231
Anti-mouse IFNγ (PE) (XMG1.2)	Biolegend	Cat# 505808; RRID:AB_315402
Anti-mouse CD11b (PE) (M1/70)	Thermo Fisher scientific	Cat# 12-0112-82; RRID:AB_2734869
Anti-mouse CD206 (APC) (MR6F3)	Thermo Fisher scientific	Cat# 17-2061-82; RRID:AB_2637420

Chemicals, peptides, and recombinant proteins

NMDI14	MCE	Cat# HY-111374
C25-140	MCE	Cat# HY-120934
Galunisertib	MCE	Cat# LY2157299
Anti-mouse PD-1 antibody	MCE	Cat# HY-P99144
Azoxymethane (AOM)	Sigma-Aldrich	Cat# A5486
Dextran Sulfate Sodium Salt (DSS)	MP Biomedicals	Cat# 160110
Actinomycin D (ActD)	Sigma-Aldrich	Cat# 129935

Oligonucleotides

Human ACTB for RT-PCR Forward: 5′-CACCATTGGCAATGAGCGGTTC-3′ Reverse: 5′-AGGTCTTTGCGGATGTCCACGT-3′	This paper	In this study
Human BAG1 for RT-PCR Forward: 5′-GTGAACCAGTTGTCCAAGACCTG-3′ Reverse: 5′-CAAGTGCTGACAACGGTGTTTCC-3′	This paper	In this study
Human GAS5 for RT-PCR Forward: 5′-CTTGCCTGGACCAGCTTAAT-3′ Reverse: 5′-CAAGCCGACTCTCCATACCT-3′	This paper	In this study
Human ATF4 for RT-PCR Forward: 5′-TTCTCCAGCGACAAGGCTAAGG-3′ Reverse: 5′-CTCCAACATCCAATCTGTCCCG-3′	This paper	In this study
Human CXCL10 for RT-PCR Forward: 5′-GGTGAGAAGAGATGTCTGAATCC-3′ Reverse: 5′-GTCCATCCTTGGAAGCACTGCA-3′	This paper	In this study
Human CCL5 for RT-PCR Forward: 5′-CCTGCTGCTTTGCCTACATTGC-3′ Reverse: 5′-ACACACTTGGCGGTTCTTTCGG-3′	This paper	In this study
Human IFNB1 for RT-PCR Forward: 5′-CTTGGATTCCTACAAAGAAGCAGC-3′ Reverse: 5′-TCCTCCTTCTGGAACTGCTGCA-3′	This paper	In this study
Human TRAF6 for RT-PCR Forward: 5′-CAACTACCTGGAGAAAACAGTGC-3′ Reverse: 5′-GCAAAATAGCTGCTGCAACATGC-3′	This paper	In this study

siRNA and shRNA targeted sequence

siRNA targeted human SMG5 Targeted sequence #1: CCUCCACACUAAGCGGCUU Targeted sequence #2: CCAGCAAUCUACAAGCCAU	This paper	In this study
siRNA targeted mouse Smg5 Targeted sequence #1: GGAACCGCCUGUCUGUGUU Targeted sequence #2: GGAGUGUGAAAGUGGAUAU	This paper	In this study
siRNA targeted human cGAS Targeted sequence #1: CCCUGGCUUUGGAAUCAAA Targeted sequence #2: GCAGGAAAGAUUGUUUAAA	This paper	In this study
siRNA targeted human STING Targeted sequence #1: CUGCUGUCCAUCUAUUUCU Targeted sequence #2: CCGGAUUCGAACUUACAAU	This paper	In this study
siRNA targeted human RIG-I Targeted sequence #1: GCCCAUUUAAACCAAGAAA Targeted sequence #2: GCUGCAGGAACUAGAAAGU	This paper	In this study
siRNA targeted human MDA-5 Targeted sequence #1: GGCCUUACCAAAUGGAAGU Targeted sequence #2: GCUGACCAAAUUAAGAAAU	This paper	In this study
siRNA targeted human MAVS Targeted sequence #1: CAUCCAAAUUGCCCAUCAA Targeted sequence #2: GACAGCAGCUCUGAGAAUA	This paper	In this study
siRNA targeted human TRAF3 Targeted sequence #1: GUUGUGCAGAGCAGUUAAU Targeted sequence #2: GGACAAACCAGCAGAUCAA	This paper	In this study
siRNA targeted human TRAF6 Targeted sequence #1: GUCCAGUUGACAAUGAAAU Targeted sequence #2: CCCAGUCACACAUGAGAAU	This paper	In this study
shRNA targeted mouse Smg5 Targeted sequence #1: GCTGTGGAGAAAGGTATACTA Targeted sequence #2: GGAACCTCAAGAGACTATATG	This paper	In this study

Primers for genotyping

Smg5 flox genotyping primers Forward-1: 5′-TTGTTCTTACCAGTGAGC-3′ Forward-2: 5′-GTTGAAGCTGTGACGTGG-3′ Reverse: 5′-TGATGTGAGCATTGTCAG-3′	This paper	In this study
Villin-Cre genotyping primers Forward: 5′-GTGTTTGGTTTGGTTTCCTCTGCATAAGA-3′ Reverse: 5′-GCAGGCAAATTTTGGTGTACG GTCA-3′	This paper	In this study

Software and algorithms

GSEA (v4.3.2)	Subramanian et al.⁵⁷ Mootha et al.⁵⁸	https://www.gsea-msigdb.org/gsea
Molecular Signature Database	Liberzon et al.⁵⁹	https://www.gsea-msigdb.org/gsea/msigdb
Sangerbox website	Chen et al.⁶⁰	http://www.sangerbox.com/tool
UALCAN online platform	Chandrashekar et al.⁶¹	https://ualcan.path.uab.edu/
Human Protein Atlas (HPA) database	Uhlén et al.⁶²	http://v15.proteinatlas.org/
Kaplan-Meier (KM) Plotter (Colon cancer)	Győrffy et al.⁶³	https://kmplot.com/analysis/index.php?p=service&cancer=colon
Kaplan-Meier (KM) Plotter (Immunotherapy)	Kovacs et al.⁶⁴	https://kmplot.com/analysis/index.php?p=service&cancer=immunotherapy
Starbase v3.0	Li et al.⁶⁵	http://starbase.sysu.edu.cn
GraphPad Prism 9.5.1	GraphPad Software	https://www.graphpad.com/
Fiji software	National Institutes of Health	https://imagej.net/software/fiji/
CaseViewer 2.4	3DHISTECH	http://www.3dhistech.com.
FlowJo v10.0.1	Becton, Dickinson and Company	https://www.flowjo.com/
PyMOL 2.6	Schrödinger	https://www.pymol.org/
MANTIS score	Bonneville et al.⁶⁶	https://doi.org/10.1200/PO.17.00073
GSVA (v2.2.0)	Hänzelmann et al.⁶⁷	https://www.bioconductor.org/packages/release/bioc/html/GSVA.html
Tumor immune dysfunction and exclusion (TIDE)	Jiang et al.⁶⁸	http://tide.dfci.harvard.edu/
Immunedeconv	Sturm et al.⁶⁹	https://github.com/icbi-lab/immunedeconv
CIBERSORT	Newman et al.⁷⁰	https://cibersortx.stanford.edu/

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Experimental model and subject participant details

Patients and samples

All clinical CRC samples were obtained with informed consent at Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University. Its Ethics Committee approved this study. Written informed consent was obtained from each patient before study initiation. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Sir Run Run Shaw Hospital, the School of Medicine, Zhejiang University (approval number 20230211-147 and approval date February 11th, 2023). Animal experimental protocols were approved by the animal care committee of Sir Run Run Shaw Hospital, Zhejiang University (SRRSH202302187).

Cell lines

We purchased the human CRC cell lines (DLD1, RKO, SW480, SW620, LoVo, HT-29, HCT116 and HCT-8), normal colon mucosal epithelial cell line (NCM460), murine CRC cell line (MC38 and CT26), and human embryonic kidney epithelial cell line HEK293T from the American Type Culture Collection (ATCC). SW480, SW620, and HCT-8 cells were cultured in RPMI 1640 medium (Cienry, CR-31800-S, China). HT-29 and HCT116 cells were cultured in McCoy’s 5A medium (Gibco, USA). RKO, DLD1, MC38, CT26, HEK293T, NCM460, and LoVo cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (Cienry, CR-12800-S, China). All of the above media contained 10% fetal bovine serum (FBS, Gibco). All cell lines were cultured in a humidified incubator at 5% CO₂ and 95% air.

Animal models

Mice

All animal experiments were carried out in compliance with Institutional Animal Care and Use Committee (IACUC) and National Institute of Health (NIH) guidelines. C57BL/6J mice, BALB/c mice and BALB/c nude mice (5–6 weeks old) were purchased from Hangzhou Ziyuan Experimental Animal Technology Co., Ltd. (Hangzhou, China). Prof. Tangliang Li (Hangzhou Normal University, Hangzhou, China) provided the Smg5^fl/fl mice on a C57BL/6J background. Meanwhile, the Villin-Cre transgenic mice on a C57BL/6J background were purchased from the Model Animal Research Center of Nanjing University (MARC, Nanjing, China). Then, Smg5^fl/fl Villin-Cre (Smg5^ΔIEC) mice were obtained by crossing Smg5^fl/fl mice with Villin-Cre mice. Mice expressing Cre recombinase under control of the Villin promoter (Villin-Cre) have been described previously.⁷¹^,⁷² This model drives Cre activity throughout the intestinal epithelium, including both the small and large intestines. Low-level Cre expression has also been reported in extraintestinal tissues. Normal reproduction and adherence to Mendel’s laws were observed in the mice. The genotyping identification procedures were performed using the PCR assays and DNA gel electrophoresis. The primers for identifying genetically modified mice are listed in the key resources table. All mice were housed in a specific-pathogen-free (SPF) facility.

AOM/DSS-induced colon cancer model

The AOM/DSS modeling cycle lasted for a maximum of 14 weeks. First, on Day 0, the 8-week-old Smg5^fl/fl mice and Smg5^ΔIEC mice were intraperitoneally (i.p.) injected with AOM (10 mg/kg body weight). After one week, the mice were given 2% DSS in their drinking water to begin DSS treatment for 1 week. The 1-week DSS treatment was repeated thrice with a 2-week interval between cycles, and DSS water was refreshed every other day. After the last DSS administration, mice were fed normal chow and drinking water for 6 weeks. Mice were sacrificed by CO₂ on about Day 100 for tumor analysis. Colon tissues were dissected from the mice, cut open longitudinally along the main axis, and flushed and cleaned with PBS to examine tumor nodules. The number of tumors was tallied, and a Vernier caliper was used to measure the size of the tumors. Parts of the colon were lysed in liquid nitrogen for protein extraction and stored at −80°C. After fixation in 4% paraformaldehyde, the remaining intestinal tract was subjected to IHC or immunofluorescence staining.

Tumor xenograft assay

To generate a subcutaneous xenograft tumor model, approximately 1 × 10⁶ cells (MC38 or CT26) were suspended in 100 μL PBS and inoculated subcutaneously into the flank of individual mice (C57BL/6J mice, BALB/c mice, or BALB/c athymic nude mice) on Day 0. Once the xenografts reached a palpable size of roughly 5 mm in diameter, the mice were evenly distributed into groups as indicated (n = 6 per group) with the treatment of NMDI14 (10 mg/kg body weight; i.p., every 2 days), C25-140 (10 mg/kg bodyweight; i.p., every 2 days), anti-IFNAR monoclonal antibody (200 μg per mouse; i.p., one day prior to treatment initiation, and every 3 days thereafter), anti-mouse PD-1 antibody (100 μg per mouse; i.p., every 3 days), Galunisertib (75 mg/kg bodyweight; oral gavage, once daily), or equivalent i.p. injections of the solvent vehicle DMSO. Additionally, to verify SMG5 function in tumor progression, 1 × 10⁶ MC38 cells stably expressing scramble shRNA or Smg5 shRNA were injected subcutaneously into the flanks of C57BL/6J mice on Day 0 (n = 4 per group). Tumor volume was measured regularly using a Vernier caliper to track tumor growth every other day. The volume of tumors was calculated using the following equation: volume = (length×width²)/2. For ethical reasons, the mice were euthanized when the experiment reached a certain number of days, tumor size reached approximately 1,500 mm³, or upon the development of skin necrosis. When all mice were sacrificed, the xenograft tumor tissues were surgically removed, weighed, and measured accordingly. We treated the collected tumor tissues with IHC staining or flow cytometry to detect the subpopulation of infiltrated immune cells.

Method details

Bioinformatics analysis of clinical databases

Data collection

The nonsense-mediated mRNA decay (NMD) signature was defined as a gene set comprising 17 established components of the NMD pathway. This includes core NMD effectors (UPF1, UPF2, UPF3A, and UPF3B),⁷³ NMD regulatory and triggering factors (SMG1, SMG5, SMG6, SMG7, SMG8, and SMG9),¹⁰ core exon junction complex (EJC) components (EIF4A3, RBM8A, MAGOH, and CASC3),⁷⁴ and auxiliary NMD-associated factors (RNPS1, ETF1, and ICE1).⁷⁵^,⁷⁶^,⁷⁷ The fragments per kilobase per million mapped reads (FPKM) profiles of the level-3 sequencing transcriptomic data for colon adenocarcinoma (COAD) patients were retrieved from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). The dataset included 41 adjacent normal colon tissue samples and 480 COAD tissue samples. To stratify tumors according to their microsatellite status, we retrieved the MANTIS scores (a computational metric for MSI with a default cutoff of 0.4) for TCGA-COAD samples from cBioPortal.⁶⁶ After excluding invalid entries, tumors with MANTIS scores of >0.4 were classified as MSI (n = 91), and those with scores of ≤0.4 were classified as MSS (n = 310). Resultingly, a total of 401 tumors were included in the CRC group. To identify APC-mutant and wild-type samples, we downloaded somatic mutation data from the GDC portal. Samples harboring non-silent mutations in the APC gene (e.g., nonsense, frameshift, missense, or splice-site mutations) were classified as mutants, and the remaining samples were classified as wild-type. We further collected corresponding clinical data from the Genomic Data Commons (GDC) portal (https://portal.gdc.cancer.gov/), including patients’ survival status, age, gender, clinical stage, and TNM classification. Samples with incomplete clinical information were excluded. Table S1 shows the clinical information of these TCGA-COAD patients.

Bioinformatics analysis

We utilized the Gene Set enrichment analysis (GSEA) software (Version 4.3.2)⁵⁷^,⁵⁸ and custom gene set for NMD, along with the HALLMARK gene set collection from the Molecular Signature Database (https://www.gsea-msigdb.org/gsea/msigdb),⁵⁹ to perform GSEA. The |Normalized enrichment scores (NES)| > 1 and false discovery rate (FDR) < 0.25 were established as the thresholds. The NMD signature scores were calculated using the ssGSEA algorithm from the GSVA R package.⁶⁷ A curated NMD gene set (as described above) was used to assess NMD pathway enrichment in each sample based on the TCGA-COAD data. The resulting ssGSEA scores (reflecting NMD pathway activity) were then compared across different clinical variables and crucial CRC subgroups, including tumor stage, T/N/M classification, microsatellite status, and APC mutation status. Volcano plots were employed to screen differentially expressed genes between multiple groups with the cutoff criteria of adj.P.Val <0.01 and |log₂FC| > 2, which were further analyzed through Gene Ontology (GO) enrichment to characterize the probable biological functions of these genes. Using the Sangerbox website (http://www.sangerbox.com/tool),⁶⁰ an online platform for data analysis and visualization, Volcano plots and the enrichment analyses of GO were executed. The mRNA and protein levels of genes in CRC were analyzed via the UALCAN online platform (https://ualcan.path.uab.edu/),⁶¹ with datasets derived from the TCGA and Clinical Proteomic Tumor Analysis Consortium (CPTAC) database, respectively. The immunohistochemistry staining results of TRAF6 expression in CRC tissues and non-cancerous tissues were obtained from the Human Protein Atlas (HPA) database (http://v15.proteinatlas.org/).⁶² Survival analyses of patients stratified according to SMG5 or TRAF6 mRNA levels were completed through the Kaplan-Meier (KM) Plotter (http://kmplot.com/analysis).⁶³^,⁶⁴ The Starbase v3.0 (http://starbase.sysu.edu.cn) was used to predict binding sites between RNA-binding proteins and the target genes.⁶⁵

Immune correlation analysis

Based on the TCGA-COAD data, we performed a series of analyses to explore the relationship between gene expression and tumor immune characteristics. Spearman’s correlation analysis was applied to assess the relationship between quantitative variables that do not follow a normal distribution. A p-value of <0.05 was considered statistically significant. Tumor mutation burden (TMB) data of COAD samples were downloaded from the TCGA database. TMB was calculated as the number of somatic mutations per megabase of coding region. To predict potential responses to immune checkpoint blockade (ICB) therapy, we employed the tumor immune dysfunction and exclusion (TIDE) algorithm, which evaluates two major mechanisms of tumor immune evasion, namely, cytotoxic T lymphocyte (CTL) dysfunction and CTL exclusion as mediated by immunosuppressive factors. Higher TIDE scores are associated with reduced efficacy of ICB therapy and shorter overall survival.⁶⁸ Immune cell infiltration was evaluated via the TIMER algorithm from the immunedeconv R package, using the TCGA-COAD gene expression data.⁶⁹ The TIMER algorithm enables the estimation of six major immune cell types and is widely used for deconvoluting bulk transcriptomic data. We also analyzed the expression of key immune checkpoint-related genes, including CD274 (PD-L1), CTLA4, HAVCR2 (TIM-3), IGSF8, ITPRIPL1, LAG3, PDCD1 (PD-1), PDCD1LG2 (PD-L2), SIGLEC15, and TIGIT, to gain further insight into the immune regulatory landscape of the tumor microenvironment. We used the CIBERSORT algorithm to estimate tumor-infiltrating immune cell data.⁷⁰ Statistical analyses were performed using the R software (version 4.0.3), and results with a p-value of <0.05 were considered statistically significant.

Lentiviral infection and transient transfection

GenePharma Company (Shanghai, China) designed and synthesized all of the small interfering RNAs (siRNAs) and corresponding negative controls, which were transiently transfected into cells with Lipofectamine RNAiMAX transfection reagent (Thermo Fisher Scientific, 13778500, USA) per the manufacturer’s guidelines. The pLKO.1 lentiviral RNAi expression system was utilized to construct lentiviral shRNA (scramble and shSmg5). Afterward, the MC38 cells were infected with lentiviral particles for 12 h and then the fresh complete medium was substituted for the virus-containing medium. The infected cells were selected and treated for 96 h with 4 μg/mL puromycin to establish stable Smg5 knockdown MC38 cell lines and control cell lines. The key resources table summarizes the sequences of siRNAs and shRNAs in the study.

Prof. Shenduo Liu (Zhejiang University, Zhejiang, China) gifted the plasmids encoding the IFNβ-luc reporter and the renilla luciferase reporter (pRL-TK-luc). Prof. Tingjuan Deng (Zhejiang University, Zhejiang, China) generously provided the pCMV-Flag-TRAF6 plasmid. The pcDNA3.1-His-Ubiquitin plasmid was presented by Prof. Qiyin Zhou (Zhejiang University, Zhejiang, China). We purchased the pCMV3-TBK1-Myc plasmid from Sino Biological (HG11023-CM, Beijing, China). Then, we generated site-specific mutants or truncation mutants of Flag-TRAF6, TBK1-Myc, and His-Ub plasmids by mutation using the wild-type plasmids as the template. According to the instructions, plasmids were transfected into the cells that were seeded overnight with the X-tremeGENE HP DNA Transfection Reagent (Roche, Basel, Switzerland). After transfection for 48–72 h, the cells were harvested for different assays.

Western blot analysis

To determine the protein expression, Western blot (WB) analysis was employed. Proteins from the cells were extracted using the RIPA lysis buffer (Beyotime, Beijing, China) and quantified through the BCA Protein Assay Kit (Beyotime, Beijing, China). Protein samples were boiled with the SDS loading buffer and separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE). Afterward, the samples were transferred to a PVDF membrane (Millipore). PVDF membranes were blocked with 5% skimmed milk dissolved in TBST for 1 h at room temperature and were then incubated for 12 h with the primary antibodies at 4°C. The blots were incubated for 1 h at room temperature with the secondary antibody labeled with peroxidase (Jackson ImmunoResearch Inc., PA, United States). The protein bands were made visible using the ECL chemiluminescence kit. To quantify the WB autoradiographs, the band intensities were measured using the Fiji software. Background subtraction was employed to minimize non-specific signals. All the bands were quantified and normalized accordingly. Specifically, for phosphorylated proteins, the signal intensity was normalized to the corresponding total protein (e.g., p-TBK1/total TBK1) to reflect the activation status. For non-phosphorylated proteins, the band intensities were normalized to their corresponding loading controls (e.g., β-actin or GAPDH). The fold changes were calculated based on relative quantifications as compared to the control group. The antibodies used in this study were listed in the key resources table.

RNA extraction and real-time quantitative PCR

For concentration measurements, we utilized the TRIzol Reagent (Invitrogen, 15596026, Shanghai, China) for total RNA extraction, and RNA concentration was measured using the NanoDrop 2000 (Wilmington, DE, USA). Reverse transcription was carried out from the respective mRNA transcripts using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, USA). Next, we used the UltraSYBR Mixture (cwbiotech, CW0957M, China) and Light Cycler 480 II systems (Roche, Switzerland) for real-time quantitative PCR (RT-qPCR) to determine the mRNA expression. The β-Actin or GAPDH levels were adopted for normalization. The relative expression of mRNAs was assessed using the 2^−ΔΔCt method. For RNA stability assays, when the density of the cells under treatment reached 80%, ActD (5 μg/mL) was added according to the indicated hours. The cells were then collected and detected by RT-qPCR, while the 18S rRNA gene was used as the internal reference gene.

MTS and Cell Counting Kit-8 assay

Cell viability was measured using either an MTS assay (CellTiter 96 AQueous One Solution Cell Proliferation Assay, Promega) or a Cell Counting Kit-8 (CCK-8) assay (YEASEN, 40203ES80, Shanghai, China) according to the manufacturer’s instructions. 5 × 10³ cells were seeded in 96 well plates, which were either pre-transfected with siRNAs for 48 h, or were treated with NMDI14 afterward. The cells were subsequently cultured for three days at 37°C in an incubator. The MTS reagent or CCK-8 solution was added to each well, and the cells were incubated for additional hours. The absorbance was ultimately measured at 490 nm for MTS, and 450 nm for CCK-8 using the BioTek Gen5 system (BioTek, USA). The optical density (OD) value was analyzed using GraphPad Prism (version 9.5.1).

Immunofluorescence and immunohistochemistry of tissue sections

We sent intestinal tumor tissues to Biossci Biotechnology Co., Ltd. (Hubei, China) for paraffin embedding and serial sectioning after fixation in 4% PFA. The company then completed the follow-up IF of the intestinal tissue sections. We captured the IF images using a triple-channel fluorescence microscope and images were visualized through the CaseViewer 2.4 software. For IHC assay, after a serial process of deparaffinization, rehydration, antigen unmasking, and blocking, the slides were incubated with primary antibodies for 1.5 hours at room temperature and then with the corresponding secondary antibodies. Afterward, the process was succeeded by 3,3′- diaminobenzidine (DAB) and hematoxylin staining successively. The staining intensity was analyzed using the Fiji software and scored as 0 (negative), + (weak staining), ++ (medium staining), and +++ (strong staining) accordingly. The fluorescent and immunohistochemical antibodies used in this study are shown in the key resources table.

Tumor sample preparation and flow cytometry

We extracted tumor-infiltrating leukocytes (TILs) from newly obtained tumor tissues using the following method. Tumor tissues were minced with sterile tissue scissors and processed into single-cell suspensions through enzymatic digestion with 1 mg/mL collagenase IV (Gibco, 17104019, USA), and 0.5 mg/mL DNase I (Solarbio, D8071, China) at 37°C for 1 h. After passing through a 70-μm filter mesh, the resulting cells were isolated with Percoll (Cytiva, 17089109, USA) gradient purification, followed by red blood cell (RBC) lysis with RBC lysis solution (Solarbio, R1010, China). To enable intracellular staining, cells were stimulated in a medium containing PMA (Sigma-Aldrich, 79346), Ionomycin (Sigma-Aldrich, I3909), as well as brefeldin A solution (Thermo Fisher Scientific, 00-4506-51) for 4 h at 37°C. Following that, they were subjected to the fixation/permeabilization buffer solution (Thermo Fisher Scientific, 00-8222-49/00-8333-56, USA). The cells were incubated in a dark room for 30 min at 4°C with various combinations of fluorophore-conjugated monoclonal antibodies. We examined the stained cells using a Beckman Coulter DxFLEX flow cytometer (Beckman Coulter, Inc., 250 South Kraemer Boulevard Brea, CA, USA) and then utilized the FlowJo v10.0.1 software to analyze the data. The fluorophore-conjugated monoclonal antibodies used in this study are shown in the key resources table.

Enzyme-linked immunosorbent assay (ELISA)

We collected blood samples from each mouse and subsequently centrifuged the blood samples at 3,000 rpm at 4°C for 15 min to collect the serum. Next, we finely ground the tumor tissues in a 9-fold homogenization medium and then centrifuged the mixture at 3,000 rpm for 10 min to collect the supernatants. All serum and tissue supernatants were stored at −80°C. Afterward, the concentrations of IFNγ were obtained using the mouse ELISA kit (MultiSciences, EK280, Hangzhou, China) as per the instructions provided by the manufacturer.

Subcellular fractionation

We carried out the nuclear/cytoplasmic protein isolation using the NE-PER Nuclear and Cytoplasmic Extraction Reagents (Thermo Fisher Scientific, 78835, USA), in accordance with the manufacturer’s guidelines. We employed Western blot analysis to detect cytoplasmic and nuclear fractions. GAPDH and LAMB1 were used as markers of cytoplasmic and nuclear proteins, respectively.

Cellular immunofluorescence

After being subjected to different treatments, RKO cells or DLD cells were seeded in a 12- or 24-well plate with cell culture slides. Then, after fixing the cells with 4% PFA for 30 min, their permeabilization took place with 0.1% Triton X-100 for 10 min. Upon blocking with 3% bovine serum albumin (BSA) for an hour, the cells were first incubated with specific antibodies at 4°C overnight and then with secondary antibodies for 1 h at room temperature. Nuclei were stained with DAPI (Thermo Fisher Scientific, D1306, USA). All images were visualized and recorded using an Olympus FV1200 SPECTRAL Laser scanning Confocal Microscope (Olympus Corp, Tokyo, Japan) and processed through the Fiji software. To quantify nuclear IRF3, we first quantified the total IRF3 cellular fluorescence intensity using the Fiji software, including both nuclear and cytoplasmic IRF3. Background signals were removed via ‘thresholding,’ and the average pixel intensity per cell was hence calculated. The nuclear IRF3 fluorescence intensity was calculated in nuclear areas as outlined via DAPI staining. The ratio of nuclear-localized protein was calculated by dividing the nuclear fluorescence intensity by the total fluorescence intensity of the same cell. The data are presented as averages ± SE and were analyzed for statistical significance via one-way ANOVA.

Luciferase activity assay

We plated HEK293T cells in 6-well plates. Then, they were transfected with plasmids encoding the IFNβ-luc reporter (firefly luciferase) and pRL-TK-luc (renilla luciferase), along with various other plasmids. After 48 h, the cells were lysed with indicated buffer, and luciferase activity was measured through the Dual-Glo Luciferase Assay System (Promega, E2940, USA). The reporter gene activity was measured by normalizing the firefly luciferase activity to renilla luciferase activity.

Co-immunoprecipitation (coIP) assay

The cells underwent two washes with ice-cold PBS and were then lysed on ice in NP-40 lysis buffer [50 mM Tris-HCl (PH 7.5), 150 mM NaCl, 1% NP-40, supplemented with cocktail protease inhibitor (Biomake, B14001, China)]. A 5% aliquot of the supernatant was saved as input. After incubating the remaining lysates with primary antibodies at 4°C overnight, we added protein A/G magnetic beads (Thermo Fisher Scientific, 88803, USA) into the lysates the following day. After rotation at 4°C for 4 h, the beads were washed with lysis buffer 5 times to eliminate the nonspecifically bound proteins. The beads were boiled in SDS sample loading buffer and then detected using Western blotting.

RNA immunoprecipitation (RIP) assay

We utilized the RNA Immunoprecipitation Kit (Geneseed, P0101, Guangzhou, China) to capture the RNA-protein complex, following the manufacturer’s instructions. Briefly, the cells were first lysed with RIP lysis buffer, and the resulting cell extracts were divided into two samples. Each sample was incubated at 4°C for 2 h, one with RIP buffer containing magnetic beads bound to human anti-UPF1 antibody and the other with normal rabbit IgG (negative control). Ultimately, we extracted and purified the RNAs from the immunoprecipitants and performed RT-qPCR analysis to confirm the presence of the binding products.

RNA pulldown assay

Biotin-labeled RNA probes were synthesized by Tsingke (Beijing, China). First, 1 × 10⁷ cells were briefly treated with lysis buffer [20 mM Tris-HCl (PH 7.5), 100 mM KCl, 5 mM MgCl2, 0.5% NP-40, supplemented with cocktail protease inhibitor and RNase inhibitor (TaKaRa, 2313B, China)] on ice for 10 min. Following the centrifugation of cell lysates, the resulting supernatant was divided into fractions and incubated with different probes at room temperature for 30 min. Streptavidin Magnetic Beads (NEB, S1420S, USA) were then added to each binding reaction for another half hour of incubation. The beads were washed 5 times, and the bound proteins were detected by Western blot.

Quantification and statistical analysis

Through the GraphPad Prism 9.5.1 software, all statistical analyses were performed. The difference between the two or more groups was analyzed using the unpaired Student’s t test or one-way analysis of variance (ANOVA). A two-sided log-rank test was employed to assess the Kaplan-Meier survival curves. All data were presented as the mean ± standard deviation (SD) or standard error of mean (SEM) values. It must be noted that the difference was deemed statistically significant if the p-value was <0.05 in the data (∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001; “-” or “ns” indicates no significance).

Published: November 19, 2025

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.xcrm.2025.102463.

Contributor Information

Hongchuan Jin, Email: jinhc@zju.edu.cn.

Jia Zhou, Email: zhoujia90@zju.edu.cn.

Xian Wang, Email: wangx118@zju.edu.cn.

Supplemental information

Document S1. Figures S1–S11 and Tables S1 and S2

mmc1.pdf^{(12.6MB, pdf)}

Document S2. Article plus supplemental information

mmc2.pdf^{(42.3MB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figures S1–S11 and Tables S1 and S2

mmc1.pdf^{(12.6MB, pdf)}

Document S2. Article plus supplemental information

mmc2.pdf^{(42.3MB, pdf)}

Data Availability Statement

•
The datasets TCGA-COAD and corresponding clinical patient information analyzed for this study can be found in GDC: TCGA-COAD (accessed on 9 November 2021). All data relevant to the experiments are included in the supplemental information. The software and algorithms employed for data analysis in this study have been previously published, with relevant citations included throughout the STAR Methods section.
•
This study does not present any original code.
•
Any additional information necessary to reanalyze the data reported in this study is available from the lead contact (Xian Wang, wangx118@zju.edu.cn) upon request.

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PERMALINK

Nonsense-mediated mRNA decay inhibits TRAF6-dependent anti-tumor immunity in colorectal cancer

Yanmei Wang

Zhuo Wang

Chaoqun Wang

Yan Wu

Jiaqiu Li

Lifeng Feng

Minyan Hao

Hongchuan Jin

Jia Zhou

Xian Wang

Summary

Graphical abstract

Highlights

Introduction

Results

NMD inhibition represses tumor progression by altering the tumor microenvironment in CRC

Figure 1.

SMG5 deficiency inhibits tumor growth and enhances anti-tumor immunity in CRC

Figure 2.

TBK1 signaling is activated by NMD suppression

Figure 3.

TRAF6 is required for TBK1 activation resulting from NMD inhibition

Figure 4.

TRAF6 interacts with TBK1 to facilitate its Lys63-linked polyubiquitination of TBK1

Figure 5.

TRAF6 mRNA is a non-canonical NMD target

Figure 6.

NMDI14 inhibition confers sensitivity to ICB therapy in CRC

Figure 7.

Discussion

Limitations of the study

Resource availability

Lead contact

Materials availability

Data and code availability

Acknowledgments

Author contributions

Declaration of interests

STAR★Methods

Key resources table

Experimental model and subject participant details

Patients and samples

Cell lines

Animal models

Mice

AOM/DSS-induced colon cancer model

Tumor xenograft assay

Method details

Bioinformatics analysis of clinical databases

Data collection

Bioinformatics analysis

Immune correlation analysis

Lentiviral infection and transient transfection

Western blot analysis

RNA extraction and real-time quantitative PCR

MTS and Cell Counting Kit-8 assay

Immunofluorescence and immunohistochemistry of tissue sections

Tumor sample preparation and flow cytometry

Enzyme-linked immunosorbent assay (ELISA)

Subcellular fractionation

Cellular immunofluorescence

Luciferase activity assay

Co-immunoprecipitation (coIP) assay

RNA immunoprecipitation (RIP) assay

RNA pulldown assay

Quantification and statistical analysis

Footnotes

Contributor Information

Supplemental information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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