Disruption of tRNA threonylation triggers RIG-I mediated anti-tumour immune response

Domain: chemistry
Published: 2026-02-25
Editorial outcome: major_revision

Abstract
Tumour-induced mechanisms of immune evasion hinder immune response to cancer, particularly in melanoma. mRNA translation, by ensuring accurate protein synthesis, regulates cancer phenotypes and immune response, but the underlying mechanisms remain unclear. Here, we reveal how O-sialoglycoprotein endopeptidase (OSGEP), catalysing the tRNA modification N6-threonylcarbamoyladenosine (t6A), drives protein homeostasis in cancer cells to maintain T-cell exclusion and prevent anti-tumour immune response. t6A-deficient melanoma cells disrupt efficient cytoplasmic translation of ANN codons (trinucleotides with A in the first position and N = any nucleotide), causing specific protein aggregation and the formation of integrated stress response-dependent stress granules. We discovered that OSGEP loss triggers melanoma regression by relocating RIG-I to stress granules, leading to its pathway activation. As a result, T-cells are recruited to the tumour site and orchestrate an anti-tumour immune response. Finally, an OSGEP-driven gene signature in melanoma patients is associated with T-cell infiltration and improved overall survival. Together, our findings position t6A tRNA modification as a promising therapeutic target for melanoma treatment.

Introduction
Immune response in cancer is a multifaceted and dynamic process that plays a critical role in the development, progression, and treatment of various malignancies1. Immune surveillance allows for the recognition and eradication of tumour cells. However, cancers have evolved sophisticated strategies to evade immune detection, promoting tumour development and posing significant barriers to the efficacy of immunotherapies2–4. Specifically in melanoma, these immune evasion strategies are particularly pronounced due to a complex immunosuppressive tumour microenvironment (TME)5,6. T-cells are fundamental components of adaptive immunity, playing key roles in antigen recognition, coordination of immune responses, and modulation of immune tolerance across diverse physiological contexts. Their ability to recognize and eliminate cancer cells through cytotoxic activity positions them as critical mediators of tumorigenesis. The effectiveness of T cell-mediated responses is often hampered by the TME, which can inhibit T-cell recruitment and activation through various mechanisms (e.g. expression of inhibitory ligands or repression of cytokine secretion)7,8.

Proteostasis (i.e. protein homoeostasis) is crucial in modulating immune response in cancer8–11. Efficient mRNA translation ensures the production of correctly folded and functional proteins to adapt to changes in the TME12. For instance, MYC induces immune evasion in liver cancer by translationally upregulating programmed death-ligand 1 (PD-L1)13. However, disrupted translation can lead to the generation of misfolded proteins, which accumulate in aggregates14, ultimately affecting the tumour immunogenicity and the course of the disease15. Yet, the precise mechanisms through which tumour cells maintain proteostasis to evade immune response remain largely unknown.

Deposition of post-transcriptional modifications on tRNAs is critical for their stability and function, with modifications in the anticodon region being particularly important for the accuracy of protein synthesis16–18. The landscape of these modifications is frequently altered in cancer, as a mechanism to adapt to both global and specific changes in mRNA translation19. The N6-threonylcarbamoylation of adenosine at position 37 (t6A) is a universally conserved modification necessary for the efficient decoding of ANN-accepting tRNAs20–23. The t6A biosynthesis pathway involves the KEOPS protein complex (Kinase, Endopeptidase and Other Proteins of Small size), with OSGEP (O-sialoglycoprotein endopeptidase) serving as the catalytic subunit responsible for the transfer of the threonylcarbamoyl moiety24. Loss-of-function mutations in genes encoding KEOPS complex subunits, including OSGEP, have been linked to Galloway–Mowat syndrome (GAMOS), a rare neuro-renal disorder characterised by a combination of steroid-resistant nephrotic syndrome and microcephaly with neurological impairments25–27. The functions of the t6A tRNA modifications in cancer and immunity are currently largely unknown.

RIG-I (Retinoic acid-inducible gene I) is a crucial sensor of innate immune system, primarily known for its role in detecting viral RNA and initiating antiviral responses in infected cells28–31. Recent studies have demonstrated that RIG-I activation triggers a potent anti-tumour immune response32,33. Upon recognition of aberrant RNAs within tumour cells, RIG-I initiates a signalling cascade that leads to the production of type I interferons, pro-inflammatory cytokines, and chemokines. This immune response enhances the recruitment and activation of immune effector cells at the tumour site34,35. Consequently, RIG-I serves as a critical immune sensor for anti-tumour immunity and represents a promising target for the development of cancer immunotherapies.

Here, we reveal that OSGEP and t6A tRNA modification are essential for the maintenance of proteostasis and thereby regulate immune reaction in melanoma. We show that OSGEP depletion in cancer cells promotes T-cells recruitment and activation at the tumour site and effectively prevents tumour growth. Mechanistically, low levels of t6A compromise the efficient decoding of ANN codons during translation. OSGEP knockdown leads to the generation of protein aggregates and initiates the activation of the Integrated Stress Response (ISR) effector Heme-Regulated Inhibitor Kinase (HRI), driving stress granule formation. RIG-I is activated within these stress granules and orchestrates a robust T cell-dependent anti-tumour response. Together, our study establishes that the t6A tRNA modification plays a critical role in preventing RIG-I activation and mitigating the anti-tumoral immune response in melanoma.

Results
At a targeted proteostasis screen identifies the essential role of OSGEP in tumour immune response
RNA modifications located at the anticodon loop of cytoplasmic tRNAs allow for the optimal decoding of mRNAs during translation and promote the maintenance of protein homoeostasis16,17,36,37. To identify anticodon tRNA modifying enzymes (tRME) required for proteostasis, we generated an esiRNA (i.e. endoribonuclease-prepared siRNA, a pool of siRNAs targeting the same mRNA sequence) screen library targeting the currently known tRMEs responsible for chemical deposition on the anticodon loop of cytoplasmic tRNAs (n=45; Fig. 1a)38. 48h after transfection, the generation of protein aggregates was measured by flow cytometry as a read out of mistranslation in two patient-derived melanoma cell lines (i.e. MM117, MM011). The esiRNA targeting OSGEP led to the highest generation of aggregates in both patient-derived lines (Fig. 1b, and Supplementary Fig. 1a). Using specific shRNAs, we validated that OSGEP depletion induced an increase of protein aggregation propensity in both MM117 and MM011 patient-derived melanoma lines, as well as in two additional murine melanoma cell lines (i.e. M1014, B16F10; Fig. 1c, d). This enzyme is the catalytic subunit of the KEOPS complex, responsible for the modification t6A in position 37 of cytoplasmic tRNANNU24–26,39 (Supplementary Fig. 1a).

B16F10 cells expressing or lacking OSGEP were then subjected to transcriptomics analyses. Interestingly, OSGEP depletion led to the activation of immune pathways, such as cytokine production, T cell-mediated immunity and immune system processes (Fig. 1e, Supplementary Fig. 1b–d, and Supplementary Datasets 1 and 2). We hypothesized that the t6A tRNA modification is necessary for tumour immune regulation by ensuring efficient mRNA translation and maintenance of proteostasis. To test this, we depleted OSGEP in B16F10 cells and implanted these cells into either immune-deficient (NOD-SCID) or immune-competent (syngeneic C57BL/6) mice. Its knockdown significantly reduced B16F10 and M1014 tumour size at day 14 in immune-competent mice but did not impact B16F10 tumour development in immune-deficient mice (Fig. 1f, g, and Supplementary Fig. 2a–d). OSGEP depletion greatly delayed melanoma development and extended survival of melanoma-bearing immune-competent individuals (Supplementary Fig. 2a–d). Further histological staining of tumours showed a decrease in proliferation (Ki67) and an increase of apoptosis (cleaved Caspase-3) in OSGEP-depleted tumours in immune-competent mice, while no difference was observed in immune-deficient mice (Fig. 1h–I, and Supplementary Fig. 2g, h). Finally, depletion of L antigen Family Member 3 (LAGE3), another member of the t6A KEOPS complex, led to a reduction of B16F10 tumour size in immuno-competent mice, without affecting tumour development in immuno-deficient mice (Supplementary Fig. 2e–f). Together, our data demonstrated that depletion of OSGEP, as part of the KEOPS complex, compromises cellular proteostasis and triggers an immune-mediated anti-tumoral response in melanoma.

OSGEP prevents T-cell recruitment and activation in the primary tumour
To decipher how OSGEP expression controls the tumour-associated immune composition, we collected immune cells by flow cytometry from control and OSGEP-deficient tumours of immunocompetent recipients at 14 days post transplantation. In line with prior reports, B16F10 tumours were poorly infiltrated and ‘immune cold’ (Fig. 2a, b, and Supplementary Figs. 3a and 4a)40,41. However, tumours lacking OSGEP displayed robust infiltration of T cells, without affecting other immune cell types, such as B cells, macrophages or neutrophils (Fig. 2a, and Supplementary Figs. 3b–e and 4b–f). Consistently, immunofluorescence CD3 staining in tumours confirmed abundant T cell infiltration in OSGEP-deficient tumours (Fig. 2b). Further flow cytometry analysis also revealed that both CD4+ and CD8+ T cells were enriched in tumours lacking OSGEP and had significantly higher capacity to produce IFNγ and TNFα (features of activated CD4+ and CD8+ effector T cells) compared with control tumours (Fig. 2c, d). Finally, to confirm the functional importance of T cells in the anti-tumour response triggered in OSGEP-defective cells, we depleted CD8+ and CD4+ T cells (i.p. injection of monoclonal anti-CD8/anti-CD4 antibodies) in C57BL/6 mice before tumour inoculation (Fig. 2e, and Supplementary Fig. 5a). T cells depletion abolished the tumour growth defect seen upon OSGEP knockdown (Fig. 2f, g). Of note, NK cell depletion could not rescue the growth of OSGEP-deficient tumours (Supplementary Fig. 5b–d). Collectively, these data indicate that effector CD4+ and CD8+ T cells actuate the rejection of tumours lacking OSGEP.

To define the clinical features of OSGEP-driven transcriptional profiles, we collected the upregulated by the depletion of OSGEP in B16F10 cells (i.e. OSGEP gene set, log2FC>0.5 and pValue>0.05; Fig. 2h; Supplementary Dataset 1). We performed an unsupervised clustering analysis which grouped melanoma patients from The Cancer Genome Atlas database (TCGA; n=473) into two clusters, according to the OSGEP gene set (Fig. 2i, j, and Supplementary Fig. 6a, b). In line with our observations that OSGEP deficiency in melanoma tumours triggers an immune phenotype mainly resolved by CD3+ T cells (Fig. 2f, g), the infiltration of CD4+ and CD8+ T-cells progressively increased with our OSGEP gene set levels (Fig. 2k, l). Additionally, the overall survival of SKCM patients correlated with the expression of the OSGEP gene set (Fig. 2m, and Supplementary Fig. 5e). Notably, OSGEP protein levels were markedly increased in melanoma lesions compared to matched adjacent skin in human melanoma biopsies (Supplementary Fig. 5f, g).

As an orthogonal confirmation of our observation, we conducted a spatial transcriptomic analysis of melanoma samples from two patients (Supplementary Fig. 6c–f). Our analysis revealed that the level of T-cell infiltration, as assessed by the T-cell signature score, was significantly elevated in Patient_01, which was classified as immunologically ‘hot’. Additionally, this ‘hot’ tumour also exhibited high levels of the OSGEP gene set (Supplementary Fig. 6g, h). We further examined the heterogeneity in OSGEP gene set expression within the tumours and correlated its spatial expression with the proximity of T-cells. Notably, in Patient_01, which displayed high levels of T-cell infiltration, we observed a positive correlation between the expression of the OSGEP gene set and the proximity to T-cells. In contrast, this correlation was absent in Patient_02, which displayed low levels of T-cell infiltration (Supplementary Fig. 6i–k).

In summary, the OSGEP signature is prognostic for T-cell tumour infiltration and overall survival in SKCM (skin cutaneous melanoma) patients. Thus, we identified a transcriptome gene signature predicting tumour immune reaction and survival in melanoma.

OSGEP supresses RIG-I signalling pathway
Changes in the TME by, for instance, the expression and secretion of cytokines and chemokines by tumour cells have been shown to modulate specific T cell recruitment42,43. To assess whether knockdown of OSGEP in melanoma cells alone was sufficient to induce migration of T cells, we performed ex-vivo co-culture experiments. CD3+ T cells were isolated from the spleens of C57BL/6 mice and seeded in Transwell®, with the bottom chamber containing control or OSGEP-deficient B16F10 cells (Fig. 3a, and Supplementary Fig. 7a). We observed that knockdown of OSGEP strongly enhanced migration of naïve T cells ex-vivo, suggesting the loss of OSGEP in melanoma cells triggers the release of chemoattractants (Fig. 3b). To investigate this further, we profiled the cytokinome of the medium of B16F10 cells depleted for OSGEP. Overall, the cytokines secreted, including the ones specifically attracting T cells44, were increased in the absence of OSGEP (Fig. 3c, and Supplementary Fig. 7b, c). Interestingly, the transcriptome of OSGEP-depleted B16F10 cells also displayed upregulation of pathways related to type I Interferon (IFN-I), defence response to virus and retinoic acid-inducible gene I like receptor (RLR) signalling (Fig. 3d). The latter is a family of intracellular pattern recognition receptors involved in the detection of viral DNA and RNA45. This activation results in the production of IFN-I and other pro-inflammatory cytokines, critical for antiviral immune response28. To assess the influence of OSGEP loss on the activation of RLR receptors, we measured the protein levels of all members of the RLR family46. While Cyclic GMP-AMP synthase (cGAS) remained unchanged, and Melanoma differentiation-associated 5 (MDA-5) and Stimulator of interferon genes protein (STING) levels varied inconsistently among cell lines, retinoic acid-inducible gene I (RIG-I) was strongly and consistently upregulated by OSGEP or LAGE3 depletion in B16F10 and M1014 melanoma cells, or A475 human melanoma cell (Fig. 3e, and Supplementary Fig. 7d). Moreover, the pathway downstream of RIG-I was activated, with increased phosphorylation levels of TANK-binding kinase 1 (p-TBK1) and nuclear localization of Interferon regulatory factor 3 (IRF3; Fig. 3f–h, and Supplementary Fig. 7e–g)47. OSGEP-depleted tumours also upregulated RIG-I expression in vivo in melanoma tumours (Fig. 3i). Finally, RIG-I levels correlated with CD4+ and CD8+ T cell infiltration in skin cutaneous melanoma tumours (Supplementary Fig. 7h–j). Together, our data reveal that OSGEP loss activates RIG-I signalling pathway.

t6A tRNA modification allows efficient decoding of ANN codons
Since t6A modification on tRNAs is required for decoding of ANN codons during mRNA translation (Fig. 4a), we asked whether the loss of OSGEP could lead to a disruption in protein synthesis. First, we assessed the level of the 52 most abundant cytoplasmic tRNA modifications by LC-MS. Knockdown of OSGEP in B16F10 cells led to a strong and specific reduction of t6A levels (Fig. 4b, and Supplementary Dataset 3). 2-methylthio-N6-threonylcarbamoyladenosine (ms2t6A) modification, the result of further thiomethylation of t6A sites by Cdk5 regulatory associated protein 1-like 1 (CDKAL1) on tRNAUUU48,49, was also sensitive to OSGEP depletion (Fig. 4b). Of note, the lack of t6A modification did not lead to changes in the tRNA pool in B16F10 cells, showing the deposition of t6A on tRNAsNNU does not affect their stability (Supplementary Fig. 8a–d).

To determine the effect of OSGEP on global mRNA translation, we performed L-homopropargylglycine (HPG) incorporation in B16F10 cells lacking OSGEP50. Interestingly, depletion of OSGEP did not affect global protein synthesis, which was in line with previously described studies21,51 (Fig. 4c). To validate this finding, we assessed the polysome profile of control and OSGEP-depleted B16F10 cells. Here, again, OSGEP depletion did not impact the polysomal fraction (Supplementary Fig. 9a).

During translation, only three codons engage with tRNAs at the ribosome’s E-site, P-site, and A-site. Cognate aminoacyl-tRNAs enter the ribosome at the A-site, representing the site of specific codon-anticodon pairing, markedly affected by the presence of modifications at the tRNA anticodon loop. To assess the effect of OSGEP on codon decoding, we mapped the ribosome occupancy of each transcript by ribosome profiling (Fig. 4d, and Supplementary Fig. 9b–d). In line with the absence of global effect of the lack of tRNA t6A modification on protein synthesis (Fig. 4c, and Supplementary Fig. 9a), we did not observe global changes in ribosome occupancy of ANN codons, at the A-site (Fig. 4e, and Supplementary Fig. 9e).

Depletion of OSGEP led to marked protein aggregation in melanoma B16F10 cells, as a result of mistranslation (Fig. 1c, d). To evaluate the specific consequence of OSGEP loss on protein synthesis, we analysed the protein composition of purified aggregates by mass spectrometry (Fig. 4f, and Supplementary Dataset 4). Proteins detected in aggregates upon OSGEP depletion were involved in structural constituents of the ribosome, glutathione binding or unfolded protein binding, confirming that protein homeostasis was affected by the lack of t6A modification (Fig. 4g). Importantly, we calculated the average ribosome stalling (RPF count/gene) for each mRNA encoding proteins found in aggregates in control and OSGEP knockdown cells. This analysis revealed a significant increase in ribosome stalling values for OSGEP-depleted aggregate mRNAs compared to controls (Fig. 4h). Moreover, the mRNAs corresponding to proteins uniquely found in aggregates of OSGEP-deficient cells exhibited increased ribosomal occupancy on ANN codons, particularly at the A-site, when compared to the ones uniquely found in shCtr cells or a random list of mRNAs (Fig. 4i–k, Supplementary Fig. 10a–c, and Supplementary Dataset 5 and 6). Similar to previous studies, knockdown of OSGEP affected differently ANN codons (Fig. 4l)21,22,27. Together, our data indicate that the lack of t6A affects the decoding of ANN codons at the A-site.

OSGEP restricts activation of RIG-I by HRI-induced stress granules
When exposed to stress, such as a temperature change and infection, cells can activate the integrated stress response pathway (ISR) to minimise cellular damage. However, the ISR pathway can also be activated by the accumulation of misfolded proteins through Heme-Regulated Inhibitor (HRI), as a result of altered translation52–55. Since loss of t6A is responsible for an increase in protein aggregates, we tested whether it induced the ISR pathway activation. Depletion of OSGEP in B16F10 or A375 cells was responsible for a strong induction of HRI protein expression (Fig. 5a, and Supplementary Fig. 11a). In line with this, we also observed an increase in the phosphorylation of the main target of HRI, alpha subunit of eukaryotic translation initiation factor 2 in both cell lines tested, despite the absence of measurable global translation repression, suggesting that the ISR induction observed is modest and selective (eIF2α; Fig. 5b, and Supplementary Fig. 11b). Similarly, depletion of LAGE3 in B16F10 cells lead to the induction of both HRI and pEIF2α (Supplementary Fig. 11c, d). Of note, HRI was not induced by proteasome inhibitors (i.e. MG132, Bortezomib) or untranslated protein response (UPR) activator (i.e. Tapsigargin) in B16F10 cells, indicating that KEOPS deficiency activates HRI independently of these pathways (Supplementary Fig. 11e, f).

The ISR pathway activation further leads to the assembly of cytoplasmic stress granule (SGs), which serve as a protective mechanism of the mRNA translation process until resolution of the stress53,54. These granules house mRNAs, small RNAs, RNA-binding proteins and other components of the translation machinery56. First, using Ras-GAP SH3-binding protein 1 (G3BP1) as a marker of SGs assembly57, we found that depletion of OSGEP in B16F10 cells triggered the formation of SGs (Fig. 5c, d, f, g). Second, in line with previous studies, we also found RIG-I colocalized in stress granules of cells lacking OSGEP58–60 (Fig. 5d, e). To test whether SGs play a central role in the activation of RIG-I pathway upon depletion of OSGEP, we generated B16F10 cells carrying the depletion of both OSGEP and HRI. Strikingly, the depletion of HRI abolished the formation of stress granules observed in OSGEP-deficient cells (Fig. 5f, g) and prevented RIG-I upregulation in OSGEP-deficient cells (Fig. 5h). As a confirmation, we also treated B16F10 cells with an inhibitor of EIF2α phosphorylation (ISRIB)61, which led to a decrease in stress granule formation in OSGEP-depleted cells (Supplementary Fig. 12a, b). Of note, staining of stress granules and protein aggregates in OSGEP-depleted B16F10 cells showed no colocalization (Supplementary Fig. 12c). Also, ISRIB treatment that blocks ISR activation and subsequent assembly of SGs failed to reduce protein aggregate formation, indicating that protein aggregates formed upon OSGEP loss precede the assembly of SGs (Supplementary Fig. 12d). Together, this reveals that loss of OSGEP drives stress granule formation and RIG-I activation via the activation of HRI pathway.

t6A tRNA modification averts tRNA recognition by RIG-I
The major function of RIG-I in eukaryotic cells is to act as a sensor of viral infection and to activate the immune response. However, upon various cellular stresses, RIG-I can also recognise RNA from the host62–64. To assess which RNA ligand is responsible for RIG-I pathway activation following loss of OSGEP, we performed an RNA-immunoprecipitation (RIP) assay using a FLAG-tagged RIG-I construct in B16F10 cells depleted for OSGEP (Supplementary Fig. 13a). First, we isolated RNA bound to RIG-I in control or OSGEP-depleted B16F10 melanoma cells to transfect wild-type (WT) cells. RIG-I and TBK1 were activated in WT cells only upon transfection with RNA bound to RIG-I from OSGEP-depleted but not control cells, confirming the immunogenic potential of endogenous RNA resulting from OSGEP depletion (Fig. 6a). We subjected the immunoprecipitated RNA-RIG-I complex from shCtr or shOSGEP B16F10 melanoma cells to total RNA sequencing. In line with previous studies, RIG-I was observed to bind various types of endogenous RNA65,66. Strikingly, tRNAs were the most differentially enriched RNA species bound to RIG-I in response to OSGEP loss (Fig. 6b). Interestingly, this enrichment included tRNAs from terminal base anticodon groups (NNU, NNA, NNC, NNG), which were uniformly upregulated, regardless of their status as targets of t6A modification (Fig. 6c, d, and Supplementary Fig. 13b–d). Together, this suggests that the loss of OSGEP broadly impacts the recognition of tRNAs by the RIG-I sensor.

To confirm the pivotal role of RIG-I in the OSGEP-induced immune response, we generated B16F10 cells carrying depletion for both OSGEP and RIG-I, effectively disabling the downstream pathway of the latter (Fig. 6e). We then transplanted the double knockdown cells into immunocompetent mice. RIG-I loss, alone, was sufficient to completely restore the growth deficit of OSGEP-depleted tumours (Fig. 6f). Moreover, the infiltration and activation of CD4+ and CD8+ T-cells were also eradicated in double knockdown tumours (Fig. 6g, h, and Supplementary Fig. 14a–f). These data show that RIG-I activation is essential to promote the T cell-dependent anti-tumoral effect seen upon OSGEP depletion.

In summary, our data reveal that OSGEP, and by extension t6A tRNA modification, prevents T cell-mediated immune response in melanoma by maintaining ANN codon decoding translation, which avoids activation of RIG-I in stress granules by tRNAs, thereby allowing tumour growth (Fig. 6i).

Methods
Ethics statement
Our research complies with all relevant ethical regulations. Our in vivo experiments are approved by the Ethical committee of the University of Liege (#2268). The study design followed the principles of the 3Rs (Replacement, Reduction, and Refinement) to ensure responsible animal use. The human study sample project was reviewed and approved by Research Ethics Committee and NHS Health Research Authority (IRAS project ID: 216310 REC reference: 16/LO/2098), sponsored by the University of Manchester

Cell culture and reagents
All cells were cultured in a humidified incubator at 37°C with 5% CO2. Melanoma cell line B16F10 (ATCC CRL-6475), A375 (ATCC CRL-1619) and Lenti-X 293 T (Sigma-Aldrich) were grown in Dulbecco’s modified Eagle’s medium (DMEM, LONZA) supplemented with 1% glutamine and 1% penicillin-streptomycin and 10% FBS (GIBCO). M1014 were obtained from Lionel Larue (Curie Institute), MM117 and MM011 were provided by Pr. G. Ghanem (Institut Bordet, Université Libre de Bruxelles)45 and were grown in HAM’s medium supplemented with 10% FBS and 1% penicillin-streptomycin. All cells were routinely tested negative for mycoplasma contamination.
Cells were treated with 5µM of MG132 (Enzo, #MG132-B12N), 15µM of ISRIB compound (Sigma-Aldrich #SML843-5MG) for 24h. Cells were treated with thapisgagin (Sigma-Aldrich #T9033) and bortezomib (medchemexpess #HY-10227) for 18h. shRNA sequences, primers sequences and references for antibodies are provided in Supplementary Dataset 7. All shRNA plasmids were obtained from Vectorbuilder. For double knockdown, B16F10 cells were first transduced with lentiviral shRNA CTR and shRNA RIG-I according to manufacturer’s instructions. These cells were transduced again 3 days later with lentiviral shRNA CTR or OSGEP to achieve double knockdown. Knockdown efficiency was evaluated by RT-qPCR and/or by western blot analysis. For overexpression of RIG-I, lentivirus construct containing FLAG-tagged RIG-I was generated by VectorBuilder. All esiRNA sequences were purchased from Sigma-Aldrich/Merck. For transient transfections, a mix of 30 nM of esiRNA and DharmaFECT 2 transfection reagent (Horizon) were added to the medium for 6h. Media were changed, and depletion was evaluated after 48h by RT-qPCR. esiHRI transient transfection with pre-designed silencer (Invivogen #AM16708) was performed using the Lipofectamine® 2000 reagent protocol.

Lentiviral infections
Lenti-X 293T cells were transfected with 12µg of the lentiviral construct of interest (shRNA or overexpressing plasmids), 5µg of VSVG and 12µg of pPsx2 using Mirus transfection reagent (Mirus™ TransIT™-LT1 Transfection Reagent, MIR2360, Fisher Scientific). All constructs are listed in Supplementary Dataset 7. After 48h, the supernatant was collected, centrifuged to remove cell debris, filtered through a 0.45µm filter and polybrene was added to the medium before transduction to target cells. The same procedure was repeated on the second day. Cells were then selected with puromycin (1µg/ml) for 48h. Knockdown or overexpression efficiency was confirmed by qPCR or western blot analysis. Three independent transductions were performed for all experiments.

Mice and treatment
Female C57BL/6 and NOD-SCID N mice were obtained from Charles River. Female mice were 6–8 weeks old at the time of tumour inoculation due to ease of handling and housing logistics. No sex-stratified analyses were performed, as the primary focus of this study was the characterisation of tumour growth dynamics under the knockdown of our protein of interest. Future studies may explore potential sex-based differences in immune response to the lack of t6A. All mouse husbandry and experiments were carried out according to the local ethics committee ULiege under the terms and conditions of the animal licence #2268. Mice were maintained under controlled environmental conditions with a 12h light/12h dark cycle, temperature of 22 ± 2°C and relative humidity of 40–60%, with free access to food and water.
1×106 B16F10, M1014 cells resuspended in PBS were subcutaneously injected into the right flank of animals. Tumour size was measured once palpable by assessing the longest dimension (length) and the longest perpendicular (width). Tumour volume was estimated with the formula (L×W2)/2. The maximal tumour size permitted by the ethical committee (1cm3) was not exceeded. For antibody treatments, antibodies were purchased from BioXcell: α-IgG (cat# BE0089) α-CD4 (cat# BE0119), α-CD8 (cat# BE0117), α-NK1.1 (cat# BE0036). Mice were treated with 500µg, 2 days prior tumour inoculation, followed by 250µg at days 3, 8 and 13 post-tumour injection via intraperitoneal injection.

Tumour preparation for flow cytometry
Tumours were extracted on day 14 post injection and dissociated in digestion media (collagenase, FBS, DNAse) with GentleMACS using digestion tumour program mTDK2, 37°C for 45min. Staining was performed according to protocols of transcription factor, intracellular and cell surface staining from Milteny’s manufacturer. Briefly, tumours were filtered through 70µM cell strainer and cells were counted. 1,000,000 tumour cells were stained with 7-AAD viability dye to exclude dead cells, washed and stained with cell surface antibodies. INFγ and TNFα staining was performed using Inside Stain kit (Miltenyi #130-090-47), according to manufacturer’s instructions. Briefly, cells were fixed and permeabilized with Fixation/Permeabilization solution during 30min, and stained with the mix of antibodies for 30min in the dark at 4°C.
All cells were washed, resuspended in FACS staining buffer for analysis on BD FACSCanto™ II. Data were analysed using FlowJo Software (Version 10). Antibodies are listed in Supplementary Dataset 7.

Migration assay
Spleen from 7 to 12-week old mice was taken and smashed manually, and T cells were isolated using Pan T Cell Isolation Kit II for mouse from Miltenyi. Briefly, splenocytes were incubated first with Biotin-Antibody cocktail for 5min at 4°C and then mixed with Anti-Biotin Microbeads for 10min at 4°C. Magnetic cell separation was performed, and negative selection containing T cells was collected. For the migration assay, 5×105 T cells in 300µL of PBS were placed in the upper chamber of Transwell® Permeable support, 6.5mm insert (24-well plate). In the lower chamber, 6×104 B16F10 cells were seeded for 48h in 500µL of supplemented DMEM. After 1h, Transwell® were carefully removed, and pictures of the lower part were taken using Leica DMIL microscope and LAS V4.11 software. Counting was performed manually using Thoma cell counting chamber and final count was calculated using the formula: Cell count = Counted cells × 104 cells/ml.

Quantification of protein aggregates
Protein aggregation was monitored by FACS using the PROTEOSTAT Aggresome detection kit (ENZO ENZI-51035-K100), according to the manufacturer’s instructions. Briefly, cells were seeded in 6-well plates; MG132 treatment was used as positive control (5µM, 18h). Cells were then collected, fixed in 4% paraformaldehyde solution for 30min, and permeabilized with 0.4% Triton X-100 for 20min. Aggregation dye was added in 1:5000 dilution in the 1x Assay buffer for 30min. Fluorescence was measured by BD FACSCanto II using L1 laser and the mean fluorescence intensity (MFI) was obtained. APF (Aggregation Propensity Factor) was calculated according to the formula APF = 100x(MFIko-MFIcontrol)/MFIko.

Protein extraction and immunoblot assay
Cells were washed two times with PBS to ensure complete removal of media. On ice, cells were lysed with RIPA lysis buffer (25mM Tris-HCl pH 8.0, 150mM NaCl, 1% NP-40, 1% sodium deoxycholate, 0.1% SDS) supplemented with Complete™ proteinase inhibitor cocktail (ROCHE, cat# 04693116001) and phosphatase inhibitor (Roche, cat# 04906837001). Cell lysates were incubated on ice for 15min before centrifuged at top-speed during 10min at 4°C. Protein determination was assessed using BCA (Bicinchoninic Acid) protein Assay Kit from Thermo Fisher (cat# 23227) and according to manufacturer’s instructions. Volume and quantity of proteins were adjusted and Laemmli buffer x5 was added, following boiling at 95°C for 5min. Samples were loaded on SDS-PAGE gels, followed by transfer on PVDF membranes (Milipore, cat# IPVH85R). Membranes were blocked in 5% milk at room temperature for 1h and incubated overnight at 4°C with the appropriate antibody. Next day, membranes were washed three times with TBST and incubated with mouse or rabbit secondary HRP antibodies in 5% milk at room temperature for 1h. Membranes were washed three times with TBST, and ECL was applied to reveal them using Amersham ImageQuant 800. Pixel density was calculated using ImageJ measurement tool.

Immunostaining
For staining of B16F10 cells in vitro, cells were fixed on a coverslip in 4% paraformaldehyde and permeabilized in PBS supplemented with 0.3% of Triton X-100. Blocking was done with 5% FBS for 1h. Primary antibody was kept overnight at 4°C in PBS supplemented with 0.3% Triton X-100 and 2% of FBS. Appropriate secondary antibody (dilution 1/1000) was added for 1h and DAPI (1/10,000) for 15min at room temperature in PBS with 0.3% Triton X-100 and 2% FBS. Images were acquired by confocal microscopy using Nikon A1R.
Extracted B16F10 subcutaneous tumours were fixed overnight with 4% paraformaldehyde, transferred to 70% EtOH and embedded in paraffin. Sections were deparaffinised and subjected to heat-induced antigen retrieval in 10mM sodium citrate buffer (pH 6). Then, slides were permeabilised for 10min with PBS containing 0.3% Triton X-100 at room temperature and washed three times for 5min in PBS. To block non-specific antibody binding, sections were incubated with blocking buffer comprising 2.5% goat serum (ImmPRESS™ kit, Vector Laboratories) in PBS with 0.1% Tween-20 (PBST) for 30min. Tissue slides were incubated with primary antibodies overnight at 4°C. To detect specific proteins of interest, slides were then incubated with primary antibodies diluted in 1% FBS in PBST at 4°C overnight. Slides were then washed three times in PBS for 5min each. To label the detected proteins, slides were incubated with the Alexa Fluor 488 and Alexa Fluor 568-secondary antibodies diluted in 1% BSA in PBST for 1h at room temperature, protected from light (1:1000; ThermoFisher). Sections were washed as before and their nuclei counterstained with DAPI (1:10,000 in PBS; Sigma-Aldrich) for 10min. Finally, sections were rinsed with PBS, and the glass coverslips mounted using fluorescence mounting medium (S302380-2; Agilent). The primary antibodies used were CD3 (1:200, #ab11089, abcam), RIG-I (1:100, #700366, Invitrogen), and IRF3 (1:100, #SC-9082, Santa Cruz), G3BP1 (1:250, #66486-1-Ig, Proteintech).
For histological analysis on murine tissues, tissue sections (4μm thick) were deparaffinized and rehydrated. For immunostaining, tissue sections were deparaffinized, rehydrated, and subsequently subjected to heat-induced antigen retrieval depending on the antibody. Endogenous peroxidase activity was inactivated with 3% H2O2. After blocking in 2.5% goat serum or BSA 3%, 0.01% Triton-X100 for 30min at room temperature, tissue slides were incubated with primary antibodies overnight at 4°C. Then, corresponding secondary antibody reagents directed against mouse or rabbit were used for detection. DAB (3,3-Diaminobenzidine) or AEC substrate was used as chromogen. Whole slides were digitally scanned using a NanoZoomer 2.0 HT (Hamamatsu).

Spatial transcriptomics
Indexed sequencing libraries were prepared from H&E-stained Visium Spatial Gene Expression Slides using the Visium Spatial for FFPE Gene Expression Kit, Human Transcriptome (10× Genomics, 1000338) according to the manufacturer’s protocol. Library quality was checked using the Fragment Analyser (Agilent). Libraries were quantified by qPCR using a KAPA Library Quantification Kit for Illumina (Roche, 07960336001). Paired-end sequencing with read lengths of 28+10+10+50bp was performed on a NovaSeq 6000 sequencer (Illumina Inc.). Data was processed through the standard Space Ranger pipeline.
Deconvolution analysis for cell type signatures in tumours was performed with the STdeconvolve package (version 1.4.0) standard workflow in R (version 4.3.0). This package performs a reference-free cell type deconvolution analysis using latent Dirichlet allocation (LDA) modelling to determine the optimal number of cell signatures. For analysis, spots with fewer than 100 transcript counts were removed, and genes that are expressed in 100% of spots or present in fewer than 5% of spots were removed. To find the optimal number of cell type signatures (topics) for each sample, a range of LDA models were fitted with between 10 and 35 cell types. The optimal number of cell type signatures (k) were determined by the model with the lowest perplexity. Marker genes for each signature were determined as the top 20 genes with the highest log2-fold change compared to other signatures (Supplementary Fig. 6c, d).
The filtered Space Ranger output was further processed and analysed in the Seurat package (version 5.1) in R. All samples were analysed with the standard Seurat spatial transcriptomics workflow individually. Firstly, spots with fewer than 100 transcripts detected were removed from samples and cell type signature (topic) percentages from STdeconvolve analysis were added as to object metadata. Cell signatures of similar cell types were added together to form main tissue signatures (melanoma, immune, endothelial, stromal, hair follicle). Each sample was normalised with the SCTransform function. T-cell score and OSGEP gene signatures were calculated using the AddModuleScore function. To calculate the distance of each spot to T cell-enriched spots, the x and y co-ordinates were extracted with the GetTissueCoordinates function. For each sample, the T-cell score was grouped into quartiles, and the spots in the highest quartile were considered T-cell-enriched. For all spots, the minimum distance to any spot enriched for T-cells was calculated by computing the Euclidean distance from each spot to each T-cell spot and recording the smallest distance.
To directly compare T-cell and OSGEP signature expression between samples, the two samples were first normalised with SCTransform before merging together. PCA was performed on the merged object, and batch effects between samples were corrected using RunHarmony function in the harmony package (version 1.2.0). Dimensionality reduction and clustering were performed on the harmony reduction.

Cytokinome analysis
Cytokine array was performed using Proteome Profiler Mouse XL (ARY028 R&D systems) and performed according to manufacturer’s instructions. In brief, cytokine membranes were activated using Array buffer. Cell supernatants were collected and incubated with cytokine membranes overnight at 4°C on rocking platform. Membranes were washed 3 times, and Detection Antibody Cocktail was added for 1h at room temperature. Membranes were washed 3 times and Streptavidin-HRP was added for 30min at room temperature. Membranes were washed 3 times, Chemi Reagent Mix was added and followed by revelation using ImageQuant LAS4000 mini. Dot intensities were analysed using the Protein Array Analyser plug-in for ImageJ and following manufacturer’s instructions.

Protein synthesis rate quantification
Cells were plated into 6-well plates in complete DMEM media for 48h. To assess translation activity, Click-IT Plus HPG Alexa Fluor 488 assay kit was used (#C10456, ThermoFisher). Briefly, L-homopropargylglycine was added to fresh media at 10µM concentration for 30min. Cells were trypsinised, washed with PBS and fixed with the Click-IT fixative. Pellets were resuspended with a mix of saponin-based permeabilization buffer and the Click-IT Plus reaction cocktail containing the fluorescent picolyl-azide dye, washed with the washing reagent and resuspended in the saponin-based permeabilization buffer. The analysis and quantification of the Alexa Fluor 488 fluorescence intensity was performed on BD FACSCanto II.

RNA sequencing
B16F10 cells were depleted for OSGEP using shRNA, with scramble shRNA serving as a control. Total RNA from three independent infections was extracted using TRIzol (Life Technologies) following the manufacturer’s protocol. RNA integrity was assessed on the Bioanalyzer 2100 with RNA 6000 Nanochips, and all samples had RIN scores greater than 9. Libraries were prepared from 500ng of total RNA using the Illumina Truseq RNA Sample Preparation kit V2. PolyA RNA was purified with polyT-coated magnetic beads, chemically fragmented, and used as templates for cDNA synthesis with random hexamers. The cDNA ends were then end-blunted, adenylated at the 3′ OH ends, and ligated to indexed adaptors. The adaptor-ligated library fragments were enriched by PCR according to Illumina’s protocol and purified. Libraries were validated on the Bioanalyzer DNA 1000 chip and quantified by qPCR using the KAPA library quantification kit. Sequencing was performed on a HiSeq 2000 with a paired-end 2×100 base protocol. Sequencing data from total RNA sequencing were pre-processed and aligned using the nf-core/rnaseq workflow (v.3.0). Briefly, a quality trimming cutoff of 10 was applied and adapters were removed automatically using TrimGalore. Pre-processed reads were aligned to the Mus musculus (GRCm38) reference genome using STAR aligner (v.2.6.1d) and quantification was performed by Salmon (v.1.4.0)[2] using the star_salmon option. Differential expression analysis was performed on the triplicates of two groups (shCTR and shOSGEP) using the packages edgeR (v.3.14)[3] and DESeq2 (v.1.30)[4] in the R environment. To identify the Differentially Expressed Genes (DEGs), the Log2 Fold-Change (log2FC) and p-values were calculated, shCTR was used as the reference. DEGs were considered up- or down-regulated according to the sign of the log2FC. A Log2(fold change) value superior to 0.5 and inferior to −0.5, and p-value<0.05 were defined as thresholds to select differentially expressed genes for further gene ontology analysis, using Toppgene software.

Protein aggregate proteomics
Cells were pelleted and washed two times with cold PBS. Pellets were lysed using cytoplasmic buffer solution (340mM sucrose, 3mM of CaCl2, 0.1mM of EDTA, 2mM MgCl2, 1mM DTT, 0.5% NP-40 and protease inhibitor in Tris-HCl pH 8) and incubated 10min on ice. Lysates were centrifuged at 4°C, 15min at 1300×g and the collected supernatants were again centrifuged at 4°C, 10min at 20,000×g. Supernatant of this fraction contains the cytoplasmic fraction and is stored at −80°C. Pellets were washed with wash buffer (Na-phosphate pH 6.8, PMSF 1mM, 2% of NP-40 and protease inhibitor in distilled water).
After aggregates isolation, pellets were resuspended in 50mM Tris buffer containing 8M urea, reduced, alkylated and digested using Lys-C/Trypsin mix (Promega) for 4h at 37°C, then the urea concentration was lowered to 1M by dilution with 50mM Tris buffer and digested overnight. The digestion was stopped by adding trifluoroacetic acid and half of the protein digest was purified using Pierce C18 Tip 10µL bed (Thermo Scientific) and dried. Peptides were suspended in 0.1% TFA and spiked with a commercial mixture of protein digest standards originated from non-human biological material: the MassPREP™ Digestion Standards (Waters), at 50 fmol of ADH per injection. This commercial standard consists of two standard mixtures (MPDS Mix 1 and MPDS Mix 2) containing protein digests of Yeast Alcohol Dehydrogenase (ADH), Rabbit Glycogen Phosphorylase b, Bovine Serum Albumin, and Yeast Enolase, present at known protein ratios, allowing for the relative quantitation of the spiked samples. Peptides were independently analysed by LC-ESI-MS/MS on an Acquity M-Class UPLC (Waters), hyphenated to a Q Exactive Plus (Thermo Scientific), in nanoelectrospray positive ion mode. The trap and analytical columns were respectively Symmetry C18 5µm (180µm×20mm) and HSS T3 C18 1.8µm (75µm×250mm; both Waters). The samples were loaded at 20µL/min on the trap column in 98/2 water 0.1% formic acid (A)/acetonitrile 0.1% formic acid (B) during 3min and subsequently separated on the analytical column at a flow rate of 600nL/min with a linear gradient of 150min (0min, 98/2 A/B v/v; 5min, 93/7; 135min, 70/30; 150min, 60/40). The total run time was 180min. Top N-MSMS method, where N was set to 12 was employed with resolution of 70,000 in MS, AGC target of 1e6 or maximum injection time of 50ms and a mass range from 400 to 1600 m/z. The parameters for MS2 spectrum acquisition were resolution of 17,500 with AGC target of 1e5 or Maximum injection time of 50ms and isolation Window of 2.0 m/z with collision energy (NCE) of 28.
MaxQuant v 1.6.17.0 was used to perform peptide and protein identification against the SwissProt database limited to Mus Musculus taxonomy (17038 sequences, June 2020) and relative quantification using LFQ normalization [J. Cox, M.Y. Hein, C.A. Luber, I. Paron, N. Nagaraj, M. Mann, Mol. Cell. Proteomics: MCP, 13 (2014), pp. 2513–2526]. Oxidation of methionine and deamidation of glutamine and asparagine were set as variable modifications, carbamidomethylation of the cysteines as fixed modification, peptide spectrum match and protein false discovery rates (FDR) were both set at 0.01.

Small RNA sequencing
For small RNA sequencing, cells were pelleted, and RNA was isolated using TRIzol Reagent according manufacturer’s instruction. RNA was sent to ArrayStar for isolation, sequencing and analysis. Briefly, RNA samples were quality controlled for quantity by NanoDrop ND-1000 spectrophotometer and for RNA integrity by Bioanalyzer 2100 or gel electrophoresis. For each sample, 100ng of total RNA was firstly dephosphorylated to form the 3-OH end. The 3-OH-ended RNA was then denatured by DMSO and enzymatically labelled with Cy3. The labelled RNA was hybridized onto Arraystar Mouse small RNA Microarray (8x15K, Arraystar) and the array was scanned by an Agilent Scanner G2505C. Agilent Feature Extraction software (version 11.0.1.1) was used to analyse acquired array images. Quantile normalization and subsequent data processing were performed using GeneSpring GX v12.1 software package (Agilent Technologies). After normalization, the probe signals having Present (P) or Marginal (M) QC flags in at least 1 out of 6 samples were retained. Multiple probes from the same small RNA (miRNA/tsRNA (tRF&tiRNA)/pre-miRNA/tRNA/snoRNA) were combined into one RNA level. Differentially expressed small RNAs between two comparison groups were identified by fold change (FC) and statistical significance (p-value) thresholds. Hierarchical Clustering heatmaps, scatter plots and volcanoplots were plotted to display small RNA expression patterns among samples by R software.

RNA modifications quantification (LC-MS)
For LC-MS-based tRNA modification analysis, cell pellets were sent to ArrayStar for tRNA isolation and sequencing. Briefly, tRNAs were isolated from total RNA checked for quality control by Urea-PAGE electrophoresis. 60–90nt bands of tRNAs were excised and purified by ethanol precipitation. Purified tRNAs were quantified using Qubit RNA HS Assay kit (ThermoFisher, Q32855). tRNAs were hydrolysed to single nucleosides, and then nucleosides were dephosphorylated by enzyme mix. Pretreated nucleoside solution was deproteinized using Sartorius 10,000-Da MWCO spin filter. Analysis of nucleoside mixtures was performed on Agilent 6460 QQQ mass spectrometer with an Agilent 1260 HPLC system. Multi-reaction monitoring (MRM) mode was performed. LC-MS data was acquired using Agilent Qualitative Analysis software. MRM peaks for each modified nucleoside were extracted and normalized to the quantity of purified tRNAs.

Ribosome profiling
Ribosome profiling was performed using Immagina Biotechnology’s RiboLace kit for ribosome pulldown and LACEseq kit for library preparation. Briefly, B16F10 cells depleted or not for OSGEP were grown to confluence in a 10cm dish and treated with 10µg/mL cycloheximide (CHX) for 5min at 37°C to arrest translation, followed by cell lysis and RNA digestion. Active ribosomes were then pulled down using a puromycin analogue (RiboLace probe) and magnetic beads, with two pulldowns performed for each sample. RNA was subsequently extracted with the acid phenol-chloroform method, followed by size selection of 25–35bp fragments in a 15% TBE-urea PAGE gel using Immagina’s PAGExt gel extraction system. RPFs were quantified with the Qubit™ microRNA Assay Kit before proceeding with the library preparation.
RPF library preparation with LACEseq involved sequential 5′ phosphorylation, ligation, and circularization steps, with small RNA purification after each step using the RNA Clean & Concentrator™−5 kit from Zymo Research. Reverse transcription and an initial PCR amplification were then performed, followed by cleanup of the PCR product using AMPure XP beads. A second PCR amplification was carried out to incorporate unique dual indexes using Immagina’s iUDI Plate. Final cleanup of the indexed library was done using the NucleoSpin Gel and PCR Clean-Up kit followed by size selection in a 10% TBE PAGE gel with PAGExt gel extraction. Library quality and size were confirmed on a Bioanalyzer 2100, and sequencing was conducted on a NovaSeq 6000 (Illumina) at the NGS platform of the GIGA Institute, with 70 million reads per sample. Sequencing data from ribosome profiling were pre-processed by trimming the LACE-seq 3′-end linker, trailing 5′ (NNNNT) and 3′-end (NNNN) degenerated sequences using Cutadapt [1] (Trim Galore wrapper v.0.6.6). Reads within a 27–33 nucleotide range (estimated by Immagina) were selected using SeqKit tool (v.2.1.0) and aligned to the Mus musculus coding transcriptome (GRCm38-v41) using STAR aligner (v.2.7.9a). Only reads with a perfect match were kept for further analysis. On average, 3.8 million reads per sample were mapped uniquely.
The optimal offset P site was determined from the 5′-ends of the selected reads and deduced from the location of the translation initiation site. The P site offset is defined specifically by looking at the global distribution of CDS start codon position (Methionine–AUG) and taking the most frequent position. We therefore selected reads with 0 and −1 frames of coding sequences and used a deduced offset P site of 15nt from the 5′-end of the reads (methodology from ref. 36). After identifying the three ribosomal sites positions in the reads, the 5′-end has been trimmed to keep only the E, P, A sites (also known as Ribosome Protected Fragments) and 3′-end flanking regions of the reads. Flanking regions are the three non-tRNA binding codons downstream of the A site, used as non-translated control regions for the codon occupancy calculation.
To assess the ribosome occupancy, the frequency of each of the codons in the ribosomal sites and flanking regions was calculated. The ribosome occupancy metric is the result of the division of the frequency of the codons in the given ribosomal site by the average frequency in the flanking regions. The average ribosome occupancy by codon or of codons by transcripts subpools was extracted.
The ribosome stalling analysis has been performed using the R package RiboDiPA on the ribo-seq triplicates of the two conditions (shCtr and shOSGEP)70. Transcripts were divided into genomic locations bins. To obtain the genomic locations of the ribo-seq data, we remapped the RPFs data to the Mus musculus (GRCm38) reference genome using STAR aligner (v.2.6.1d) and used mapped reads BAM files for the analysis. In the context of this study, we assessed, for each gene of interest, the ribosome stalling (number of reads mapping) at each location and calculated an average gene ribosome stalling by pooling altogether these locations values. To identify a change in the ribosome stalling, the Log2 Fold-Change was calculated for each gene, shCtr being used as the reference.
All the steps described to perform the ribosome occupancy calculations were implemented in custom python (v.3.8.1) and bash scripts.

Polysome profiling
B16F10 melanoma cells, depleted or not for OSGEP were gently scraped in phosphate-buffered saline containing 100µg/ml cycloheximide on ice. After centrifugation at 200×g for 5min at 4°C, the cells were lysed in hypotonic buffer containing 2.5mM MgCl2, 5mM Tris pH 7.5, 1.5mM KCl and 1× protease inhibitors. Subsequently, CHX, dithiothreitol, RNAse inhibitor and Triton X-100 followed by sodium deoxycholate were sequentially added to solubilize cytosolic and endoplasmic reticulum-associated ribosomes. The lysates were then centrifuged at 16,000×g for 7min. The cytosolic lysates were loaded onto a non-linear sucrose gradient (from 5% to 34% to 55%) and ultracentrifuged at 220,000×g for 2h at 4°C, using the SW60 rotor in an Optima XPN-80 ultracentrifuge. RNA abundance was measured throughout the polysome gradient using a UV detector (Teledyne Isco), with detection at 254 nm.

RIG-I/RNA complex immunoprecipitation
B16F10 cells with stable expression of FLAG-tagged RIG-I and of shRNA Ctr, OSGEP #1 or OSGEP #2 were grown on 100mm culture dishes at 80% confluency. Cells were washed three times with cold PBS (Phosphate-Buffered Saline) and lysed in RIP buffer (150mM KCl, 25mM Tris pH 7.4, 5mM EDTA, 0.5mM DTT, 0.5% NP40) supplemented with protease inhibitors for 30min at 4°C. Lysates were cleared by centrifugation at 10,000×g for 15min at 4°C. 1mg of lysates was mixed with anti-FLAG, anti-A/G protein-conjugated magnetic beads (MedeChemExpress, #HY-K0207, #HY-K0202 respectively) according manufacturer’s instruction. The binding reaction was incubated on rotation overnight at 4°C. Beads were washed 4 times with RIP buffer and elution was completed with buffer elution A (0.15M glycine, pH 3). After 10min, neutralization buffer (1M Tris-HCl pH 8.0) was added, followed by incubation with 10mg/ml of proteinase K (Fischer Scientific, #10103533) at 55°C for 1h. The RNA bound to FLAG-RIG-I was extracted using TRIzol protocol (Invitrogen). 1µg/mL of RNA extracted from shCtr or shOSGEP depleted cells was transfected back on wild-type B16F10 using the lipofectamine 2000, according to manufacturer’s protocol.
Library preparation was performed using 10ng of total RNA following the Illumina Stranded Total RNA Ligation with Ribo-Zero Plus protocol (Illumina). The final library profiles were assessed using the QIAxcel Advanced System (Qiagen), and concentrations were measured by qPCR using the SYBR Fast KAPA Kit. Indexed Libraries were pooled in equimolar ratios, and the pooled library was sequenced on the NovaSeq 6000 platform using an S2 flow cell with paired-end 150bp reads, with 50 million fragments sequenced per sample.
Raw reads were demultiplexed and adapter-trimmed using Illumina bcl2fastq conversion software v2.20. They were processed within our custom NextFlow RNAseq pipeline inspired from nf-core/rnaseq. We used TrimGalore (v0.6.7) to trim a single base at the 5′ and 3′ ends of each read and to perform a quality-based trimming after removing poly-G tails. Then, we did the STAR (v2.6.1d) alignment and Salmon quantification with the Mouse GRCm39 reference genome and the annotations from Ensembl release 112. Quality was assessed by MultiQC (v1.9) report. Raw counts were analysed using the DESeq2 package (v1.44.0) in R environment (v4.4.2), with the default methodology. Differential expression analysis was conducted, and pairwise comparisons were made between experimental conditions (e.g. shCtr vs. shOSGEP1). Results were filtered for adjusted p-values<0.01. Gene biotypes were annotated using Ensembl’s BioMart database. Statistical tests and results were conducted using the build-in DESeq2 functions.
Unmapped sequences from RNA sequencing were mapped to the Mus musculus mature tRNA reference genome (GRCm39) obtained from the Genomic tRNA database (GtRNAdb 2.0). Due to computing limitations, each sample was randomly subsampled to one million sequences using the sample function of the Seqtk tool (v.1.2). The mapping on the tRNAs has been performed using the STAR tool (v.2.7.10a), following Alex Dobin’s recommendations regarding tRNA alignment. Identification of the mapped tRNA isodecoders mapped was done with a custom Python script (v.3.10.13) using the Pysam (v.0.22.0) library. Briefly, after retrieving the Mus musculus tRNA isodecoder set from GtRNAdb, every mapped was recognized and counted. To homogenize the results, the percentage of tRNA isodecoders per sample was calculated from the counts.

In silico analyses using TCGA-HNSC datasets
RNA expression levels, clinical as well as follow-up data were downloaded from the TCGA-SKCM (n=473) cohort (https://portal.gdc.cancer.gov/) in November 2019. For clustering analysis, expression values of the top 100 differentially expressed genes from RNA-seq of shCtr and shOSGEP B16F10 cells were ln(x+1) transformed and clustering of TCGA patients was performed using Euclidean distance and Ward (unsquared distances) linkage using Clustvis 2.0. Heatmaps with hierarchical trees were generated by the web tool ClustVis58. The infiltration score of CD4+ and CD8+ cells in SKCM-TCGA tumours was obtained from the TIMER score database (http://timer.cistrome.org).

Sample sizing and collection
Graphs were generated using Prism 10 (GraphPad) or R studio (2023.09.1+494). No statistical methods were used to predetermine sample size, but at least three samples were used per experimental group and condition. Data distribution was tested for normality before using parametric tests. The number of samples is represented in the graphs as one dot per sample. If no individual samples are shown more than 5 samples per condition were used. Samples and experimental animals were randomly assigned to experimental groups. Sample collection was also assigned randomly. Sample collection and data analysis were performed blindly whenever possible. Whenever possible, automated quantifications were performed using the appropriate software. All statistical tests were performed using Excel 2016 or Prism 10. The statistical analyses performed for each experiment are indicated in figure legends.

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

Data availability
RNA sequencing data using B16F10 cells are available under the accession number GSE286709. Quantitative proteomics data on protein aggregates are available on PRIDE under the number PXD059835 (https://www.ebi.ac.uk/pride/). Spatial transcriptomics dataset is available under the accession number GSE316760. All other sequencing data are deposited on GEO under the accession number GSE286704 for the ribosome profiling and GSE286715 for the RNA seq after Flag-RIG-IP (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE286715). Results are in part based on TCGA-SKCM data generated by the TCGA Research Network (http://cancergenome.nih.gov/). Source data are provided as a Source Data file. All other data supporting the findings of this study are available from the corresponding author on reasonable request (Pierre.Close@uliege.be). Source data are provided with this paper.

Code availability
TrimGalore: https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ TrimGalore is a Perl wrapper based on two canonical tools (Cutadapt and FastQC) for adapters and quality-based trimming. TrimGalore’s usage improves consistency and repeatability of analysis. No newly generated code is presented. RSamtools: https://bioconductor.org/packages/release/bioc/html/Rsamtools.html. R package of Samtools was used for BAM files manipulations in this project (i.e: Metagene plot). No newly generated code is presented. GenomicAlignments: https://bioconductor.org/packages/release/bioc/html/GenomicAlignments.html R/Bioconductor package was used for the storage, manipulation and representation of short genomic alignments. In this project this package allowed us to perform the binning of genomic alignments for the ribosome stalling analysis. No newly generated code is presented. Pysam: https://github.com/pysam-developers/pysam: this used for the manipulation and storage of BAM/SAM files. Here, it was used to store BAM files for the tRNA pools analysis. No newly generated code is presented. ToppGene: https://toppgene.cchmc.org/: ToppGene suite is a canonical tool to perform Gene Ontology analysis, gene set enrichment and candidate gene prioritization. It was used for the RNA-sequencing data analysis. No newly generated code is presented. STAR: https://github.com/alexdobin/STAR: STAR is a canonical software for mapping RNA-seq reads to a reference genome. It was used for the RNA-seq and ribosome profiling in this project. No newly generated code is presented. DESeq2: https://bioconductor.org/packages/release/bioc/html/DESeq2.html: it was used for the normalization, visualization, analysis of RNA-seq data and differential expression analysis. Used for the RNA-sequencing and ribosome profiling analysis. No newly generated code is presented. EdgeR: https://bioconductor.org/packages/release/bioc/html/edgeR.html: R package developed for the differential expression analysis of RNA-sequencing data. It was used here for statistical analysis in the ribosome profiling analysis. No newly generated code is presented. GSVA: https://www.bioconductor.org/packages/release/bioc/html/GSVA.html: Gene Set Variation Analysis R package allowing gene set and pathway enrichment on single samples. No newly generated code is presented. Source data files are provided with this paper in supplementary data files. Gating strategies for FACS analysis is available in the corresponding supplementary figures.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, the authors identify the role of OSGEP inactivation in promoting T-cell infiltration in vivo in melanoma cells. Mechanistically, they show that OSGEP function is required to prevent the formation of misfolded protein granules due to translation defects. In turn, these trigger the formation of cytoplasmic stress granules and the release of pro-inflammatory cytokines through the activity of HRI and RIG-1, respectively. While the effect of OSGEP depletion on protein homeostasis was previously described, its effect on T-cell activation and the anti-tumoral response is novel and highly interesting. Despite this, certain aspects of the study remain unclear. The authors should address these before the manuscript can be considered for publication.\nMajor points:\n1.In Figure 2, the authors identify a gene expression signature mediated by OSGEP downregulation in melanoma cells. They focused on upregulated transcripts, showing that the same upregulation correlates with good prognosis in melanoma patients. Did the authors investigate whether there is any negative correlation between this signature and OSGEP expression?\n2.Since t6A on tRNA can be detected through mass spectrometry, the authors should investigate the abundance of this modification in primary melanoma samples and untransformed nevi or normal skin tissue, and its correlation with OSGEP expression and prognosis.\n3.In Figure 4, the authors show that OSGEP inactivation, while it doesn’t affect global translation levels, increases ribosome occupancy on ANN codons of mRNA encoding for proteins found in protein aggregates. It is not clear how the translation of a relatively small number of mRNAs is affected by OSGEP depletion. The authors should analyze the ribosome footprinting dataset to identify potential ribosome stalling or frameshift specifically occurring on affected mRNAs. Are total levels of misfolded proteins affected by OSGEP depletion?\n4.Connected to my previous point, in Figure 2K, it is shown that increased occupancy only occurs on specific ANN codons. The authors should report occupancy for individual codons in Figures 4e, h, i, and j. How can the authors justify this observation?\n5.In order to better characterize their mechanism, the authors should perform rescue experiments by transfecting modified or unmodified ANN tRNAs in OSGEP-depleted cells and investigate protein granule formation and downstream activation of HRI and RIG-1.\n6.In Figure 6, it is reported that OSGEP depletion increases RIG-1 interaction with tRNAs. Again, the authors did not observe any specificity for OSGEP tRNA targets. Did they analyze specific enrichment of individual OSGEP targets?\n7.Is the presence of unmodified NNA tRNAs sufficient for RIG-1 activation? To test this, the authors should transfect unmodified NNA tRNAs in melanoma cells and investigate RIG-1 activation.\nMinor points:\n1.In Figure 1, the levels of Ki67 and Caspase 3 in tumors grown in immune-deficient mice should be reported.\n2.In Figure 3F, the immunoblotting analysis of M1014 cells shows differences in HSC70 normalization. The authors should include a better-normalized Western blot or perform densitometric quantification of the current one.", "reviewer_2": "Summary/General Comments:\nThis study presents a timely and relevant research topic, reveals the key role of OSGEP-mediated t6A tRNA modification in melanoma immune evasion. The authors elegantly demonstrate that loss of OSGEP disrupts protein translation, triggers stress granule formation, and activates RIG-I signaling, ultimately driving T-cell recruitment and tumour regression. Additionally, the identification of an OSGEP-related gene signature correlates with increased T-cell infiltration and better survival, highlighting t6A modification as a potential therapeutic target in melanoma.\n---\nMajor/minor Comments:\n1. Overall, the manuscript is well written, and the study presents novel and impactful biological insights with clear clinical relevance.\n2. The transcriptomics data analysis is comprehensive and well-integrated with the experimental findings. However, I wonder about the rationale for selecting only the top 100 upregulated genes following OSGEP depletion in B16F10 cells. Would the main findings and biological interpretation remain consistent if different thresholds (e.g., top 10, 20, 50, or even 500 genes) were applied? It would be helpful if the authors can clarify on the robustness of their results to this parameter selection.\n3. Some minor issues need clarification:\n-In Figure 1, the term esiRNA appears — can the authors clarify or briefly explain this term upon its first usage?\n- There is a general lack of abbreviation definitions throughout the manuscript. Please ensure all abbreviations are defined at first mention for clarity.\n4. Regarding the statistical analysis, it would be more appropriate to report adjusted p-values (FDR-adjusted p-values) rather than exact p-values alone, particularly in large-scale transcriptomics analyses, to better control for false discovery rate (FDR) and reduce potential false positives.", "reviewer_3": "This manuscript presents a novel and compelling mechanistic link between tRNA modifications, proteostasis, and immune surveillance in cancer.\nCarefully executed study that identifies a novel role for the t6A tRNA modification enzyme OSGEP in immune evasion and tumor progression. The authors convincingly demonstrate that loss of OSGEP induces proteostasis defects, leading to RIG-I activation and a CD4+/CD8+ T cell-mediated anti-tumor response in melanoma. The mechanistic insights are supported by in vitro, in vivo, and transcriptomic data, and the clinical relevance is validated using patient-derived datasets. The manuscript is overall clear and comprehensive. The figures are well-organized, and the data supports the authors' conclusions. We believe this work will be a valuable contribution to the community. However, before publication, we encourage the authors to carefully address both the major and minor comments provided.\nMajor comments\n1. Are other members of the KEOPS complex involved in this phenotype?\n-The manuscript focuses exclusively on OSGEP, but does not assess whether other KEOPS subunits (e.g., TP53RK, TPRKB, LAGE3, GON7) contribute to the observed immune activation.\n-Would an OSGEP IP help clarify whether it acts independently or as part of the KEOPS complex in this context?\n2. Figure 3e, f; 5a, b. Have the authors tested human melanoma cell lines?\n-Cell lines could increase the translational relevance of the findings.\n3. The study adress that RIG-I activation upon OSGEP loss is mediated by tRNA accumulation. However, given that silencing HRI or ISRIB treatment suppresses RIG-I activation, could the authors clarify whether tRNA detection is sufficient on its own, or whether additional cytoplasmic events (e.g., stress granule formation, aggregation) are required to make tRNAs immunogenic?\nMinor comments:\n-Codon occupancy analysis (Figure 4e): The legend of Fig. 4e refers to codon occupancy in general terms, but the main text specifies that the analysis focuses on the A-site. Since ribosome profiling allows analysis at different ribosomal sites (A, P, E), it is important to clearly indicate in the figure legend that codon occupancy was measured at the A-site.\n-RIG-I RIP-seq (Fig. 6a-c): the number of biological/technical replicates used for sequencing is not specified.\n-While the authors state that ribosome occupancy on ANN codons is increased in OSGEP-deficient cells, especially in mRNAs encoding aggregated proteins, Extended Fig. 8e does not show a clear or statistically significant increase in A-site occupancy on ANN codons.\n-In Extended Figure 6c, the heatmap suggests there are changes in some tRNA expression between shCTRL and shOSGEP. This appears inconsistent with the statement in lines 213–215 (‘Of note, the lack of t6A modification did not lead to changes in the tRNA pool in B16F10 cells, showing the deposition of t6A on tRNAs NNU does not affect their stability’) that the tRNA pool remains unchanged and stable. The authors should clarify whether the changes shown in Fig. 6c are statistically or biologically significant, and how they reconcile this with their claim that t6A does not affect tRNA stability.\n-In Figure 6d, the authors state that tRNAs from terminal base anticodon groups (NNU, NNA, NNC, NNG) were “uniformly upregulated.” However, the figure only shows the relative proportions of these groups among RIG-I-enriched tRNAs, without comparison to the total tRNA pool or to non-enriched species. The term “uniformly upregulated” may be misleading and should be clarified.", "reviewer_4": "In their study \" Disruption of tRNA threonylation triggers RIG-I mediated antitumour immune response\" Dziagwa et al. observe that knock-down of O-sialoglycoprotein endopeptidase (OSGEP) which catalyses the tRNA modification N6-threonylcarbamoyladenosine (t6A) leads to RIG-I activation in melanoma cells.\nThe authors performed an RNAi screen against tRNA modifying enzymes in patient-derived melanoma cell lines (i.e. MM117, MM011) and monitored protein aggregates as a read-out of mistranslation. Here, repression of OSGEP caused the strongest mistranslation response. This finding was validated by shRNAs in MM117, MM011 and in murine melanoma cell lines M1014, B16F10. OSGEP is responsible for the modification t6A in position 37 of the cytoplasmic tRNAs \"tRNANNU \". Repression of OSGEP reduced tumor size in immune competent but not in immune deficient (NOD-SCID) mice. While B16F10 tumours were poorly infiltrated, OSGEP deficient tumours had a robust infiltration of T cells, partially by NK cells, but not B cells, macrophages or neutrophils. T cell but not NK cell depletion abolished the tumour growth defect of OSGEP knockdown tumours. Upregulation of genes induced by OSGEP deficiency correlated with tumor survival in SKCM patients. Knockdown of OSGEP enhanced migration of naïve T cells ex-vivo. Further data reveal that knockdown of OSGEP upregulates RIG-I expression and RIG-I signaling/activation. Interestingly, the depletion of OSGEP did not impair global protein synthesis as monitored by polysome profiles. However, RNAi of t6A affected the decoding of ANN codons at the A-site and the integrated stress response pathway was induced via HRI upregulation and eIF2a phosphorylation. The authors also observed colocalization of RIG-I with G3BP1 in cytosolic stress granules upon RNAi of OSGEP.\nThe depletion of HRI abolished the formation of stress granules observed in OSGEP-deficient cells (Fig. 5f-g), and also prevented RIG-I activation in OSGEP-deficient cells. By contrast pharmacologic inhibition of stress granule formation did not prevent the formation of protein aggregates.\nKnock-down of RIG-I in OSGEP deficient cells rescued immune evasion and prevented T cell infiltration in B16F10 tumours. RIG-I/RNA-complex IP revealed enrichment of tRNA binding to RIG-I upon OSGEP depletion. However, not the N6-threonylcarbamoyladenosine containing tRNAs were enriched in the RIG-I binding RNA pool, indicating that RIG-I recognizes endogenous tRNA independently of t6A modification in stress granules upon HRI activation.\nMajor comments\nThis is a very interesting study. It would be crucial to know how RIG-I activation works.\n1.However, it remains unclear whether a tRNA modification dependent stress response that activates RIG-I is a results of missing RNA modifications leading to RIG-I recognition or if the conditions of stress lead to a different RIG-I activation by endogenous RNA or independently of any RNA recognition. Since binding to RIG-I is necessary but not sufficient for RIG-I activation the changed binding of tRNAs to RIG-I is only a hint but not a proof for RIG-I activation by tRNA and it is unexpected that RIG-I would recognize endogenous tRNAs. The question is whether OSGEP depletion induce the generation of RIG-I ligands or if HRI activity in all cases leads to recognition of endogenous RNA by RIG-I and that enrichment of RIG-I binding tRNA represents an incidence.\na) The authors should induce HRI activation by other mechanisms (e.g. by proteasome inhibitors, bortezomib or MG132, PMID: 21090173) and check if HRI activation in general leads to higher tRNA binding to RIG-I in B16F10 melanoma cells or if this effect is specific for OSGEP depletion.\nb) The authors should purify the RNA bound to RIG-I and total RNA from OSGEP depleted cells and WT cells and stimulate WT cells with these RNAs. If OSGEP deficiency produces RIG-I ligands, the RNA of OSGEP deficient cells but not of ctrl shRNA cells should activate RIG-I in WT cells. I\n2. eIF2alpha phosphorylation should cause global translational inhibition. How can a polysome profile be unaffected if eIF2a is phosphorylated?\nThe authors should use another read-out to confirm that global protein synthesis is not influenced (e.g. SUnSET assay).\n3. Is it excluded that OSGEP is required for other modifications than t6A?\nMinor comments\nWhy does RIG-I activation not induce cell death/growth reduction by cell autonomous mechanisms.", "reviewer_5": "The manuscript entitled \"Disruption of tRNA threonylation triggers RIG-I mediated anti-tumour immune response\" presents an intriguing study exploring how alterations in tRNA modifications may influence protein aggregation, leading to activation of the RIG-I pathway and subsequent induction of type I interferon responses that enhance antitumor immunity. The authors provide a coherent set of experiments supporting the central hypothesis and demonstrate mechanistic links between tRNA modification, stress responses, and immune activation. However, there are several major concerns that limit the impact and novelty of the study.\n1. Limited Cell Line Validation:\nThe mouse model findings are derived primarily from a single cell line, which substantially weakens the generalizability of the conclusions. Validation across additional cell types would be necessary to support the broader relevance of the proposed mechanism.\n2. Neglect of Potential Neoantigenic Consequences:\nAlterations in tRNA modifications are likely to influence translational fidelity and could result in neoantigen generation. This possibility is not explored, nor is its potential contribution to antitumor immune responses addressed. This represents a significant gap in the mechanistic understanding of how tRNA modifications may affect immune surveillance.\n3. Lack of Novelty in RIG-I/Type I Interferon Axis:\nAlthough the mechanistic connection between protein aggregation and RIG-I activation is of interest, the involvement of the RIG-I/type I interferon pathway in antitumor immunity has been reported in multiple previous studies. Without a compelling new insight or clinical relevance, the novelty of this work remains limited."}, "author_response": "We would like to sincerely thank the reviewers for their careful evaluation of our work and their constructive comments. We are very pleased that the novelty and solidity of our study were recognized, and we greatly appreciate the insightful suggestions that helped us further really improve the manuscript. We systematically addressed all major and minor points raised, either by performing new experiments or by providing additional analyses and clarifications. The revised version of the manuscript now includes a substantial amount of new data and figures that refine the mechanistic understanding of how OSGEP regulates tumor immunity and further support the translational relevance of our findings. In summary, we provide the following additional evidence: 1) Involvement of t6A tRNA modification in the OSGEP phenotypes: We performed additional experiments depleting LAGE3, another essential KEOPS subunit, confirming that the OSGEP phenotypes result from the loss of t6A tRNA modification rather than unrelated effects. 2) Ribosome stalling. Using RiboDiPA pipeline, we demonstrate that OSGEP depletion induces ribosome stalling on mRNAs encoding proteins accumulating in aggregates. 3) We show that small RNAs generated upon OSGEP depletion are sufficient to activate RIG-I in wild-type cells, highlighting their immunogenic potential. 4) Human cell line and additional model: We validated our main findings in human melanoma cells and in an independent melanoma mouse model. 5) Transcriptomic data: we strengthened the analysis and description of the transcriptomic data. In addition, we now provide spatial transcriptomic data from melanoma patient samples, supporting translation relevance of our observations. 6) We have clarified the methods and conclusions across the paper and performed all requested experiments to ensure robust validation of our conclusions. Please find below our detailed point-by-point responses to the reviewer’s questions."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors addressed my major and mino points, the manuscript can be now considered for publication.", "reviewer_2": "Reviewer Report\nThe authors have responded to reviewer feedback; however, concerns remain regarding transparency and consistency between the rebuttal letter and the revised manuscript.\nA) While the authors assert in the rebuttal letter that the concerns have been addressed and the relevant sections updated, these changes are not visible in the Figure 2 legend or the main text. This inconsistency calls into question the reliability of the reported revisions.\n#The transcriptomics data analysis is comprehensive and well-integrated with the experimental findings. However, I wonder about the rationale for selecting only the top 100 upregulated genes following OSGEP depletion in B16F10 cells. Would the main findings and biological interpretation remain consistent if different thresholds (e.g., top 10, 20, 50, or even 500 genes) were applied? It would be helpful if the authors can clarify on the robustness of their results to this parameter selection.\nWe apologize for the initial wording ambiguity. Rather than using a fixed \"top 100 transcripts UP\" cutoff, our analysis applied thresholds of fold change (FC) > 0.5 and p-value < 0.05, which yielded approximately ~120 upregulated genes. Among these, 94 were matched in the TCGA dataset, forming the final OSGEP gene set. To improve transparency, we have updated Figure 2h–i by removing the “top 100 transcripts UP” label and replacing it with the precise criteria: \"FC > 0.5, p < 0.05\". The methods and figure legends have been updated accordingly. We thank the reviewer for highlighting this, which allowed us to strengthen the rigor and clarity of our analysis.\n# h. Volcano plot of the expression of transcripts 656 in shCtr versus shOSGEP B16F10 cells and generation the OSGEP gene set signature 657 consisting of the top 100 genes upregulated. i. Heatmap of unsupervised clustering 658 identified 2 clusters of patients with SKCM (TGCA) according to the top 100 upregulated 659 genes from the OSGEP gene set signature (cluster 1, n = 251; cluster 2, n = 222)\nB) The authors have addressed the minor issues raised previously.", "reviewer_3": "The authors have fully addressed all our previous concerns with appropriate new experiments, improved statistical analysis, and clearer figure legends. The mechanistic model connecting t6A deficiency to stress-granule-dependent RIG-I activation is now much more convincing. The addition of LAGE3 knockdown, human melanoma validation and RNA immunogenicity tests significantly strengthen the manuscript’s translational relevance.\nWe recommend only minor additional clarifications:\n1. Regarding sex reporting in animal experiments, Nature Communications requires explicit disclosure of the sex of the mice used, even if no sex-based analysis was performed. In the “Methods - Animal models” section (currently stating only the strain and age), the authors should indicate whether C57BL/6 and NOD-SCID mice were males, females, or mixed, and justify the absence of sex-stratified analyses in accordance with SAGER guidelines.\n2. A brief explanatory note in the Results accompanying Fig. 4c/d and Extended Data Fig. 9 would help clarify why eIF2α phosphorylation despite the absence of measurable global translational repression, noting that the ISR induction observed is modest and selective.", "reviewer_4": "The manuscript improved a lot! Minor comment: It would be great if the read-out of a stimulation with RIG-I ligands from a RIG-I pull-down would not be RIG-I expression (Figure 6a. ). Since RIG-I is an ISG I believe that the conclusions are correct, however this read-out would confuse the reader and one could speculate that the ligand influences RIG-I protein stability. Better show IFIT, ISG15, STAT1/2 phsphorylation or MX protein western blot as a read-out of type I IFN induction.", "reviewer_5": "While the novelty of the work remains somewhat limited, the authors have sufficiently addressed all other concerns raised in the previous review.", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We would like to sincerely thank the reviewers for their careful evaluation of our work and their constructive comments. We are very pleased that the novelty and solidity of our study were recognized, and we greatly appreciate the insightful suggestions that helped us further really improve the manuscript. We systematically addressed all major and minor points raised, either by performing new experiments or by providing additional analyses and clarifications. The revised version of the manuscript now includes a substantial amount of new data and figures that refine the mechanistic understanding of how OSGEP regulates tumor immunity and further support the translational relevance of our findings. In summary, we provide the following additional evidence: 1) Involvement of t6A tRNA modification in the OSGEP phenotypes: We performed additional experiments depleting LAGE3, another essential KEOPS subunit, confirming that the OSGEP phenotypes result from the loss of t6A tRNA modification rather than unrelated effects. 2) Ribosome stalling. Using RiboDiPA pipeline, we demonstrate that OSGEP depletion induces ribosome stalling on mRNAs encoding proteins accumulating in aggregates. 3) We show that small RNAs generated upon OSGEP depletion are sufficient to activate RIG-I in wild-type cells, highlighting their immunogenic potential. 4) Human cell line and additional model: We validated our main findings in human melanoma cells and in an independent melanoma mouse model. 5) Transcriptomic data: we strengthened the analysis and description of the transcriptomic data. In addition, we now provide spatial transcriptomic data from melanoma patient samples, supporting translation relevance of our observations. 6) We have clarified the methods and conclusions across the paper and performed all requested experiments to ensure robust validation of our conclusions. Please find below our detailed point-by-point responses to the reviewer’s questions."}]

Decision letter