Coronaviruses reprogram the tRNA epitranscriptome to favor viral protein expression

Domain: chemistry
Published: 2026-02-19
Editorial outcome: major_revision

Abstract
Coronaviruses genomes are enriched in suboptimal A- and U-ending codons, which are typically associated with reduced translation efficiency due to limited cognate tRNA availability. How coronaviruses efficiently express their proteins despite this limitation remains unclear. By analyzing their codon usage, we identify four tRNA modifications—inosine (I), queuosine (Q), 5-methylcarboxymethyluridine/5-methylcarboxymethyl-2-thiouridine (mcm5U/mcm5s2U), and 5-methylcytidine/5-formylcytidine (m5C/f5C)—as essential for decoding their suboptimal codons. Notably, SARS-CoV-2 and HCoV-OC43 infections, representing severe and mild human infections, respectively, reprogram these modifications to favor viral protein synthesis. Mechanistically, this reprogramming was driven by altered expression of the corresponding tRNA modifying enzymes. Since both viruses induced DNA damage and oxidative stress—known to similarly alter Q, mcm5U/mcm5s2U, and m5C/f5C modifications to favor expression of stress response proteins—our findings support that coronavirus genomes have adapted to the tRNA modification landscape under stress conditions. Overall, coronaviruses orchestrate a codon-specific reprogramming of the host tRNA modification landscape, highlighting a conserved strategy that optimizes translation efficiency and represents a promising target for pan-coronavirus antiviral therapy development.

Introduction
Coronaviruses pose a major global health threat due to their ability to cross species barriers and establish infections in humans1. These zoonotic transmissions, exemplified by the COVID-19 pandemic, can give rise to novel viral strains with unpredictable consequences2. Gaining insights into how coronaviruses exploit host cell machineries to multiply is crucial for the development of novel antiviral approaches. A key strategy involves their exploitation of the host translation machinery. The genome of coronaviruses consists of a single-stranded, positive-sense(+) RNA, ranging from 26 to 30 Kb, that encodes for 23 to 32 viral proteins. Upon entry into the cytoplasm, these proteins are directly translated from the genomic (+) RNA or from subgenomic (+) RNA transcribed during infection3. Their efficient and timely expression is essential for viral replication and expansion. For this, coronaviruses, like all viruses, rely entirely on the host translation machinery4. Despite this reliance, their genomes are enriched in suboptimal codons and consequently should be translated with poor efficiency. The redundancy of the genetic code allows multiple codons (synonymous codons) to code for the same amino acid. However, the usage of these synonymous codons is not random; rather, it is universally biased and organism-specific. In mammals, highly expressed genes preferentially use G- and C-ending codons, whose cognate tRNAs are highly abundant, whereas coronavirus genomes are enriched in A- and U-ending codons, whose cognate tRNAs are scarce5,6. This codon bias is predicted to slow down translation elongation and reduce protein expression efficiency. How coronaviruses achieve efficient genome translation in mammals despite their suboptimal codon usage remains an unresolved and fundamental question.

The redundancy of the genetic code arises through two key mechanisms. First, isoacceptor tRNAs, distinct tRNA molecules that carry the same amino acid but have different anticodons, enable the recognition of different synonymous codons. For example, the six synonymous codons specifying Leucine (Leu) are recognized by distinct tRNA isoacceptors. Second, some tRNA molecules exhibit flexibility in codon recognition, allowing a single tRNA to pair with multiple codons. This flexibility primarily results from wobble base pairing between the third position of the codon in the mRNA and the first position of the anticodon in the tRNA (N34), known as the wobble position7. tRNAs deliver the amino acids to the ribosome by pairing their anticodons (located at positions 34, 35 and 36) with complementary codons in mRNAs (positions 3, 2, and 1, respectively). While positions 1 and 2 follow strict Watson-Crick base pairing rules, the third position allows for non-standard base pairing, which can be further regulated by chemical modifications at position 348. For example, uridine at position 34 (U34) can pair with both adenine (A) and guanine (G). Chemical modifications at U34 modulate this flexibility, either enhancing or restricting non-canonical base pairing.

tRNA levels and functions are regulated by a wide array of chemical modifications collectively known as the tRNA epitranscriptome. Position 34 is a hotspot for these modifications, hosting a wide range of chemical modifications that ultimately enable a limited set of anticodons to efficiently decode the synonymous codons9. Importantly, tRNA modifications are dynamically regulated in response to environmental changes and cellular stress conditions, allowing cells to fine-tune translation efficiency and adapt to fluctuating metabolic demands. We have recently shown that infection with chikungunya virus (CHIKV), another (+)RNA virus with an A-ending codon bias, triggers a DNA damage response that ultimately leads to the overexpression of KIAA1456, a tRNA-modifying enzyme. This enzyme catalyzes the mcm5U modification at U34, favoring the decoding of the suboptimal Glutamic acid (Glu) GAA, Lysin (Lys) AAA, Glutamine (Gln) CAA, and Arginine (Arg) AGA codons over the optimal G-ending counterparts10. These changes reprogram codon optimality, selectively enhancing the translation of DNA damage stress response genes and the CHIKV RNA genome, both highly enriched in these A-ending codons. Hence, CHIKV codon usage optimally aligns with the reprogrammed tRNA modification landscape induced by the stress response to efficiently express the stress response proteins. Whether this strategy is also employed by coronaviruses and whether additional tRNA modifications might influence coronavirus protein expression are unknown. To address these fundamental questions, here we conducted a comprehensive analysis of the interplay of coronaviruses with the host tRNAome. Our approach combined bioinformatic analyses of coronavirus codon usage in the context of host tRNA modifications with Liquid Chromatography–Tandem Mass Spectrometry (LC–MS/MS) and modification-induced misincorporation tRNA sequencing (mim-tRNAseq) analysis to profile the tRNA epitranscriptome and tRNA abundance in SARS-CoV-2 and HCoV-OC43 infected cells, as representatives of high- and low-pathogenic human coronaviruses, respectively.

We found that SARS-CoV-2 and HCoV-OC43 infections reprogram the tRNA epitranscriptome to enhance viral protein expression by altering the levels of four tRNA modifications that affect the decoding of more than half of their enriched codons. Mechanistically, these changes correlate with parallel shifts in the expression of the corresponding tRNA-modifying enzymes. Remarkably, these codons are highly conserved across the coronavirus genera.

Results
Coronavirus genomes are enriched in codons whose decoding is influenced by tRNA modifications
Among the four coronavirus genera, only Alphacoronavirus and Betacoronavirus includes species that infect humans. The Alphacoronavirus genus comprises HCoV-229E and HCoV-NL63, both associated with mild infections. In the Betacoronavirus genus, HCoV-OC43 and HCoV-HKU1 also cause mild illnesses, whereas SARS-CoV, MERS-CoV and SARS-CoV-2 are highly pathogenic. To systematically analyze the codon usage of Alphacoronaviruses and Betacoronaviruses, we first examined the relative synonymous codon usage (RSCU), grouping them by their host species and, separately, focusing solely on the human-infecting ones (Fig. 1A–C). Both Alphacoronaviruses and Betacoronaviruses, including the human-infecting group, were similarly enriched in suboptimal A- and U-ending codons (Fig. 1A–C) over G- and C-ending codons, with the Leu-UUG codon being a notable exception.

Three additional features stood out. First, as previously observed in human- and bat-infecting coronaviruses11, there is a distinct preference for U-ending over A-ending codons throughout all coronaviruses, particularly in comparison to other human-infecting (+)RNA virus genera (Supplementary Fig. 1). Second, certain specific U ending codons, such as Proline (Pro) CCU, Arg-CGU, Alanine (Ala) GCU, Glycine (Gly) GGU, Valine (Val) GUU and Serine (Ser) UCU, are particularly enriched across coronaviruses. Third, within human-infecting coronaviruses, low-pathogenic strains exhibited a higher enrichment in some U-ending codons (i.e., Gly-GGU, Phenylalanine (Phe) UUU, Ser-AGU, Val-GUU) and in the Leu-UUG codon than high-pathogenic strains (Supplementary Fig. 1). Next, we focused on human-infecting coronaviruses to analyze codon bias from an amino acid perspective by comparing the synonymous codons (Fig. 1D). For simplicity, human-infecting coronaviruses will be referred to as coronaviruses hereafter.

We evaluated how many codons enriched in the coronavirus genome might be influenced by modifications that occur at the 34 wobble position of tRNAs. Indeed, the decoding of 48% of codons enriched in coronavirus genomes is affected by four tRNA modifications: I, Q, mcm5/mcm5s2U, which occur exclusively at position N34, and m5C, which can also occur at other tRNA positions besides N34. The I modification hinders the decoding of eight U-ending codons (Ala-GCU, Arg-CGU, Isoleucine (Ile) AUU, Leu-CUU, Pro-CCU, Ser-UCU, Threonine (Thr) ACU, or Val-GUU), which are among the most highly enriched throughout coronaviruses (Fig. 1D). When unmodified, the corresponding tRNAs decode only the U-ending codons. However, when I-modified, the synonymous A- and C-ending codons are also decoded, as I base pairs with U, C, or A (Fig. 1E)12. This expansion of decoding capacity would disfavor the decoding of coronavirus codons. The Q modification and its derivatives, mannosyl-Q (manQ) and galactosyl-Q (galQ), favor the decoding of four U-ending codons (Asparagine (Asn) AAU, Aspartic acid (Asp) GAU, Histidine (His) CAU, and Tyrosine (Tyr) UAU), with galQ found in human tRNA Tyr, and manQ found in human tRNA Asp (Fig. 1D)13. Since the corresponding tRNAs are absent in humans, their decoding relies on Q modifications at G34 in the anticodon loop of tRNAs decoding their synonymous C-ending codons14,15. When Q-modified, G34, which normally pairs with C or with near-cognate Q-decoded codons (such as Glu-GAA and Glu-GAG), can also base pair with U, favoring decoding of coronavirus codons (Fig. 1F). The mcm5U/mcm5s2U modifications favor the decoding of four A-ending codons (Arg-AGA, Gln-CAA, Lys-AAA and Glu-GAA) over the synonymous G-ending counterparts. When unmodified, U34 pairs with both A and G due to wobble pairing. However, when modified, pairing with A is favored, thereby enhancing decoding of coronavirus codons (Fig. 1G)10,16,17. Altogether, the decoding of 75% of the U-ending codons and 42% of the A-ending codons enriched in coronaviruses are influenced by tRNA modifications at position 34. Finally, efficient decoding of Leu-UUG, the only G-ending codon exceptionally enriched in coronavirus genomes, is favored by the tRNA modification m5C and its oxidated forms 5-hydroxymethylcytosine (hm5C) and f5C when added at position C34 in the corresponding tRNA18,19. In addition, f5C modifications also expand the tRNA recognition to Leu-UUA, which is also enriched in coronaviruses20.

While I34 modification has been shown to be very stable, the levels of Q, mcm5U/mcm5s2U and m5C/hm5C/f5C modifications are upregulated under stress conditions18,21–23. This coordinated interplay between the tRNA epitranscriptome and stress responses allows the rapid and coordinated expression of stress-response proteins, which are enriched in codons whose decoding is favored by these modifications. Specifically, Q, mcm5U/mcm5s2U, and m5C have been reported to be overexpressed in response to DNA damage (DDR) (mcm5U/mcm5s2U)24 and oxidative stress responses (all)18,21,25. This suggests that coronavirus codon usage has evolved to exploit the altered tRNA epitranscriptome triggered by virus-induced stress responses. To test whether coronavirus infection triggers stress responses, we used SARS-CoV-2 and HCoV-OC43 as models of high- and low-pathogenic human coronaviruses and analyzed DNA damage-associated protein levels and oxidative stress markers. Hereafter, SARS-CoV-2 infections were conducted in Calu3 lung epithelial cells, an established model for studying SARS-CoV-2 infection due to their high expression of Angiotensin-Converting Enzyme 2 (ACE2) receptors, facilitating efficient viral entry and replication. For HCoV-OC43, MRC5 lung fibroblasts were chosen because they support the replication of low-pathogenic coronaviruses. In both infections, we observed increased DNA damage and oxidative stress, as indicated by the upregulation of key stress biomarkers detected via western blot (Fig. 2A, B). In the DNA damage stress response, the Ataxia-Telangiectasia Mutated (ATM) or/and the Ataxia-Telangiectasia and Rad3-related (ATR) are activated, leading to the phosphorylation of various substrates, including the H2AX histone variant (γH2AX) and the kinases themselves. Notably, γH2AX can also be phosphorylated in response to certain oxidative stress conditions26. We observed that γH2AX was upregulated in both infection conditions, beginning at 36 hours post-infection (hpi) for SARS-CoV-2 and between 48 and 72 hpi for HCoV-OC43. Further analysis revealed phosphorylation of ATM, but not ATR, under both coronavirus infections (Fig. 2A, B), as shown previously in SARS-CoV-2 infected h2h7 cells27. In the oxidative stress response, the p38 mitogen-activated protein kinase (p38 MAPK) plays a key regulatory role. The phosphorylated form of this kinase (p-p38) is considered a biomarker of oxidative stress and other stressors such as UV irradiation and inflammation28. Both SARS-CoV-2 and HCoV-OC43 infections induced p38 phosphorylation, starting at 36 hpi for SARS-CoV-2 and 24 hpi for HCoV-OC43 (Fig. 2C, D). Furthermore, ROS production was confirmed by directly measuring ROS levels in infected cell (Supplementary Fig. 2A, B). These findings suggest that coronaviruses have adapted their codon usage to thrive in a tRNA environment in which stress responses are activated. Consistent with this, previous studies have shown that inhibitors of the DNA damage stress response pathway and antioxidant impair coronaviruses infection29–33. Furthermore, inhibitors of the DNA damage stress response reduce both p-p38 levels and coronavirus protein expression (Supplementary Fig. 2C). Collectively, our analyses uncover novel features of coronavirus codon usage and suggest that these may have evolved, at least in part, in response to altered host tRNA environments triggered by coronavirus-induced stress responses. To further address this fundamental issue, we comprehensively analyzed alterations in the tRNA landscape during coronavirus infection, focusing on both tRNA modifications and abundance.

Coronaviruses alter the host tRNA epitranscriptome in vitro and in vivo
To investigate alterations in tRNA modifications during coronavirus infection, and to determine the optimal timepoint when the viruses were fully replicating while the cells remained viable, we first analyzed the infection kinetics of SARS-CoV-2 in Calu3 cells and of HCoV-OC43 in MRC5 cells (Supplementary Fig. 3). For SARS-CoV-2, this timepoint was 32 hpi (Supplementary Fig. 3A–C), whereas for HCoV-OC43, it was 48 hpi (Supplementary Fig. 3D–F). At these timepoints, tRNAs were extracted from non-infected and coronavirus-infected cells and analyzed using LC-MS/MS to identify and quantify tRNA modifications (Fig. 3 and Supplementary Data 1). Both coronaviruses induced statistically significant changes in the tRNA epitranscriptome, as indicated by the log2 fold changes between infected and non-infected cells (Fig. 3A). In SARS-CoV-2 infected cells, these changes included three tRNA modifications at position 34 in the anticodon loop (Fig. 3A, black stars). In HCoV-OC43 infected cells, five tRNA modifications at position 34 of the anticodon loop (Fig. 3A, black stars), along with four additional modifications at other tRNA residues (Fig. 3A, yellow stars). Three modifications at position 34 exhibited the same altered pattern in both viruses: down regulation of I and f5C and upregulation of mcm5U (Fig. 3B–D). However, in SARS-CoV-2-infected cells, significant changes were observed in 2′-O-methyl-inosine (Im) instead of I. While I and its derivative Im appear to share similar functions, Im is less characterized and has been implicated in tRNA stability34. The observed downregulation of I/Im and upregulation of mcm5U favor the decoding of U- (Fig. 1D, E) and A-ending codons (Fig. 1D, G) that are highly enriched in coronaviruses. mcm5U can undergo further modification to mcm5s2U in tRNAs; however, its detection by LC-MS/MS is challenging due to its instability. Although statistically significant changes in mcm5s2U levels were not detected at the time of analysis, we observed its upregulation at 48 hpi post-SARS-CoV-2 infection, suggesting a transition from the mcm5U to mcm5s2U at later time points (Supplementary Fig. 4A). Importantly, mcm5U upregulation was also observed in vivo in Syrian hamsters infected with SARS-CoV-2. Three female and three male Syrian hamsters were infected, and two days post-infection, the upper right lobe of the right lung was collected for tRNAs analyses. Three uninfected females and three uninfected males were used as mock-infected controls (Fig. 3E). While qPCR analysis of SARS-CoV-2 RNA revealed variability in infection levels among the samples (Supplementary Fig. 4B), LC-MS/MS analysis of tRNA modifications showed a consistent increase in mcm5U levels in all samples, particularly in those that were the most highly infected (Fig. 3E). Lastly, f5C downregulation is predicted to hinder the decoding of Leu-UUG/UUA, enriched in coronaviruses (Fig. 1D, H). However, the precursor m5C, which stabilize the interaction with Leu-UUG codon, exhibit an opposite trend, potentially compensating for f5C loss. In addition, m5C/hm5C/f5C modifications also occur in mitochondrial tRNA-iMet, where they facilitate recognition of the Ile-AUA codon as methionine, ensuring efficient mitochondrial translation initiation19 (Supplementary Fig. 4C). Given that f5C is present in both cytoplasmic and mitochondrial tRNAs, and LC-MS/MS analysis cannot discriminate between cytosolic and mitochondrial tRNA pools, the observed changes may reflect mitochondrial stress responses rather than direct effects on viral protein translation.

Among the six statistically significant tRNA modification changes detected exclusively in HCoV-OC43-infected cells, two were located at position N34, while the others occurred at other tRNA positions (Fig. 3A, F and Supplementary Fig. 4D). Modification changes at position N34 included the upregulation of Q and the downregulation of 5-carbamoylmethyluridine (ncm5U) (Fig. 3F). The upregulation of Q aligns with coronavirus codon usage, as this modification is required to decode a subset of U-ending codons that are particularly enriched in coronaviruses (Fig. 1D). Recent studies suggest that Q modification, as f5C, may also occur in mitochondrial tRNAs35,36. Studies in yeast have shown that the ncm5U modification is added to U34 of tRNAs decoding Ala-GCA, Pro-CCA and Val-GUA to ensure proper decoding. Although these codons are present in coronavirus genomes, they are less enriched compared to their synonymous U-ending counterparts. Since the formation of mcm5U and ncm5U derives from the same precursor (Supplementary Fig. 4E), it is not unexpected that an increase in mcm5U coincides with a decrease in ncm5U. For tRNA modification changes not located at position N34, we observed an upregulation of 2′-O-methyladenosine (mA), a modification that enhances tRNA stability, and a down regulation of N1-methyladenosine (m1A), N2-methyladenosine (m2A), N1-methylinosine (m1I) and N6-threonylcarbamoyladenosine (t6A). These modifications are involved in tRNA folding and stability (m1A, t6A), recognition by aminoacyl-tRNA synthetases and ribosomes (m1A, t6A) and codon-anticodon interaction (m2A, m1I, t6A) (Supplementary Fig. 4D). As some of these modifications have not yet been assigned to specific tRNAs or may modify the same tRNA molecule in multiple nucleotide positions, it remains challenging to determine their positive or negative effects on coronavirus protein expression.

Collectively, our findings indicate that the alterations in tRNA modifications at the wobble position induced by SARS-CoV-2 and HCoV-OC43 infections enhance the decoding of codons that are highly enriched in their genomes, potentially conferring a translational advantage over host codons.

Coronavirus-induced tRNA modification changes are driven by altered expression of tRNA modifying enzymes
To gain insights into the mechanisms driving these alterations, we assessed whether changes in tRNA modifications correlated with changes in the expression of their corresponding modifying enzymes. Protein levels of these enzymes were examined in Calu3 and MRC5 cells infected with SARS-CoV-2 and HCoV-OC43, respectively, at various time points using western blot (Fig. 3G–J). Consistent with the LC-MS/MS-detected reduction in I/Im modification levels in tRNAs under both infection conditions, ADAT2 expression, a key component of the ADAT2/ADAT3 complex responsible for I modification, was downregulated37. ADAT2 protein levels declined from 36 hpi for SARS-CoV-2 and 48 hpi for HCoV-OC43 (Fig. 3G). The f5C modification pathway begins with the methylation of C34 to m5C, catalyzed by NSUN3 in mitochondrial tRNA-iMet and by NSUN2 in cytoplasmic tRNA Leu under stress conditions. This is followed by the oxidation of m5C to hm5C and subsequently to f5C, mediated by ALKBH119. Here after, we used the standard nomenclature for tRNAs, which denotes the amino acid and the anticodon. For example, tRNA Leu CAA recognizes the Leu-UUG codon. NSUN2 levels remained unchanged during SARS-CoV-2 infection but moderately increased in HCoV-OC43 infection from 48 hpi onwards (Fig. 3H). However, ALKBH1 expression was vastly downregulated in both infections (Fig. 3H), aligning with LC-MS/MS data showing a marked reduction in f5C modifications (Fig. 3C). A similar correlation was observed between the upregulation of mcm5U/mcm5s2U and Q tRNA modifications and the corresponding upregulation of their modifying enzymes. The mcm5U/mcm5s2U modification is mediated by KIAA1456 or its paralog ALKBH8 (Fig. 1G)10,38–40. Upon SARS-CoV-2 infection, KIAA1456 expression increased, while both KIAA1456 and ALKBH8 were upregulated during HCoV-OC43 infection (Fig. 3I). Q modification is catalyzed by the QTRT1/QTRT2 enzyme complex41,42. When quantifying QTRT1 expression, a strong upregulation was observed in HCoV-OC43 but not in SARS-CoV-2 infected cells (Fig. 3J), consistent with the LC-MS/MS results (Fig. 3A).

To establish a causal link between the alteration of these enzymes and viral protein expression, we manipulated the expression of selected tRNA modifying enzymes. These experiments were performed using HCoV-OC43 infection in A549 cells because the observed changes in tRNA modifications were conserved across both viruses analyzed, transfection efficiency in Calu3 and MRC5 cell lines (used for SARS-CoV-2 and HCoV-OC43 infection, respectively) is poor, and A549 cells support efficient HCoV-OC43 replication and transfection (Supplementary Fig. 3D, F). Consistent with a functional link between tRNA epitranscriptome remodeling and viral protein production, siRNA-mediated knockdown of ELP3 (mcm5U modification pathway) and QTRT1 (queuosine modification pathway) significantly reduced viral protein levels (Fig. 4A, B, D, E). ELP3 was selected for silencing because it acts upstream of KIAA1456 and ALKBH8 in the mcm5U modification pathway and is strongly upregulated during HCoV-OC43 infection (Fig. 4A). Similarly, overexpression of ADAT2 (A-to-I modification enzyme) or ALKBH1 (f5C pathway) also led to a pronounced decrease in viral protein expression (Fig. 4G, H). In contrast, NSUN2 silencing had no detectable effect (Fig. 4C, F). This lack of effect, together with the observed downregulation of ALKBH1 during infection (Fig. 3H) and the strong inhibition of viral protein production upon its overexpression, suggests that viral infection primarily modulates the mitochondrial NSUN3–ALKBH1 pathway rather than the cytosolic NSUN2-dependent m5C modification. This interpretation is further supported by the absence of significant changes in global tRNA m5C levels analyzed by LC-MS/MS during infection (Fig. 3A). Together, these findings demonstrate that alterations in mcm5U, Q, and I tRNA-modifying enzymes during infection not only align with viral codon usage but also functionally contribute to coronavirus protein production.

To further confirm the link between coronavirus infection, tRNA modifications and viral protein expression, we analyzed codon frequency in host transcripts translation-repressed (TR) versus translation-activated (TA) in ribosome profiling datasets from SARS-CoV-2–infected Calu3 cells43. We reasoned that coronavirus-induced alterations in tRNA modifications would modify codon optimality, thereby influencing not only viral but also host mRNA translation. Consistently, we found that, similar to coronavirus mRNAs, TR mRNAs were enriched in codons preferentially decoded by I-modified tRNAs (Supplementary Fig. 5A–C). In contrast, host TA mRNAs were enriched in codons preferentially decoded by tRNAs bearing I-unmodified, mcm5U and Q modifications. Moreover, ribosome occupancy analyses revealed reduced occupancy at these codons in line with enhanced translation elongation during infection (Supplementary Fig. 5D–F).

Interestingly, tRNA-modifying enzymes responsible for introducing the mcm5U/mcm5s2U modification are enriched in codons that improve their own decoding efficiency44. This creates a feedback loop that amplifies or reduces their expression in response to changes in their activity. To study if this phenomenon extends to other tRNA modifying enzymes, we analyzed the RSCU of the human ADAT2 (Fig. 5A, B), QTRT1 (Fig. 5C), KIAA1456/ALKBH8 (Fig. 5D), and NSUN2 (Fig. 5E) enzymes and of related proteins from the same pathways. In all cases except QTRT1/QTRT2, we found an enrichment in the codons whose translation they enhance.

Finally, to determine whether the expression of tRNA-modifying enzymes occurs at the transcriptional or translational level during infection, we analyzed their mRNA and protein abundance. qPCR analyses revealed that the mRNA levels were decreased (Supplementary Fig. 6A–F), whereas western blot analyses showed a concomitant increase in the protein levels (Fig. 3G–J and Supplementary Fig. 6G). These results suggest that expression of tRNA-modifying enzymes is primarily regulated at the translational level rather than transcriptionally during infection. Overall, our findings reveal a link between coronavirus codon usage, the observed alterations in tRNA modifications and in the corresponding tRNA-modifying enzymes, and viral protein expression.

Coronaviruses reprogram modifications in pre-existing tRNAs without altering their abundance
To determine whether coronavirus-induced alterations in tRNA modifications arise from changes in tRNA abundance or from modification of pre-existing molecules, and to identify possible alterations in the abundance of specific tRNA species, we employed mim-tRNAseq45,46, a high-resolution method capable of quantifying tRNA abundance and modification status. tRNAs isolated from the same samples used for LC-MS analyses were processed for mim-tRNAseq. Quality control metrics, including alignment statistics and principal component analysis (PCA), confirmed high-quality sequencing data (Supplementary Figs. 7 and 8). For SARS-CoV-2, PCA revealed separate clusters for infected and non-infected samples, indicating distinct tRNAome alterations (Supplementary Fig. 7C, D). However, for HCoV-OC43, PCA did not show clear differences between infected and non-infected samples (Supplementary Fig. 8C, D). In SARS-CoV-2-infected cells, of the 398 cytosolic tRNA species quantitatively resolved by mim-tRNA-seq, 24 displayed differential expression, with changes of less than one order of magnitude between mock-infected and SARS-CoV-2-infected samples (Fig. 6A, C). Importantly, none of these transcripts corresponded to the tRNAs affected by I, mcm5, Q, or f5C. When grouping them by the same anticodon (isoacceptors), three were upregulated (tRNA Thr TGT, tRNA Cys GCA, and tRNA Lys CTT) and seven downregulated (tRNA Ser GCT, tRNA Ser AGA, tRNA Ser TGA, tRNA Ser CGA, tRNA Leu CAA, tRNA Leu TAA, and tRNA Leu CAG) (Fig. 6B, D). In contrast, we found no significant changes in tRNA abundance in HCoV-OC43-infected MRC5 cells (Fig. 6E, F). When this occurs, PCA analysis typically cluster by the similarity of barcodes46 (Supplementary Fig. 8C, D).

The observed changes in tRNA abundance in SARS-CoV-2 infected cells uncovered interesting features. The upregulation of tRNA Thr TGT correlates with the high abundance of its cognate codon (Thr-ACA) in the SARS-CoV-2 genome, reflecting viral translational demands. Notably, this tRNA has been reported to be upregulated as part of the stress response47. In addition, the Lys-AAG codon, which corresponds to the upregulated tRNA Lys CTT, is also enriched in proteins associated with stress response, particularly short-term heat stress48. While the Lys-AAG codon is present at similar levels in both HCoV-OC43 and humans, its frequency is reduced in SARS-CoV-2. The upregulation of tRNA Cys GCA is not linked to enriched viral codons, however, cysteine proteases are critical for SARS-CoV-2 infection49. Regarding the downregulated tRNAs, all belong to the type II tRNAs, which are substrates for Schlafen 11 (SLFN11) and Schlafen 12 (SLFN12) endonucleases50. These enzymes are induced by the interferon (IFN) response as part of the innate antiviral defense or in response to DNA damage51–54. Consistent with the downregulation of type II tRNAs in SARS-CoV-2 infection, but not in HCoV-OC43 infection, SARS-CoV-2 infection triggered an IFN response, as indicated by the expression of IFN-stimulated genes and the strong upregulation of SLFN11 and SLFN12 (Supplementary Fig. 9A–C). In contrast, HCoV-OC43 did not induce a proper IFN response and only led to a slight activation of IFNB1 at 72 hpi, likely due to the DNA damage response (Supplementary Fig. 9B–D). These differences are linked to the cell lines used for SARS-CoV-2 and HCoV-OC43 experiments.

In addition to quantifying tRNA abundance, mim-tRNAseq can detect some specific modifications at defined tRNA positions45,46. In SARS-CoV-2 infected cells, four tRNA modifications were notably downregulated at position 14 in tRNA Phe GAA, position 20a in tRNA Ile AAT, and position 37 in tRNA Ala TGC and tRNA Lys TTT (Fig. 6G). The one at position 14 in tRNA Phe GAA likely corresponds to m1A, while position 20a in tRNA Ile AAT is typically modified to acp3U, enhancing tRNA stability and ribosomal interactions9,55. At position 37, modifications include m1G in tRNA Ala TGC, which stabilizes codon-anticodon interactions, and t6A in tRNA Lys TTT, essential for efficient codon recognition9,55. Similar patterns were observed for m1A in tRNA Phe GAA and t6A in tRNA Lys TTT in HCoV-OC43-infected MRC5 cells, with decreased m1A in tRNA Phe GAA and t6A in tRNA Lys TTT (Fig. 6G and Supplementary Fig. 10). These were also downregulated in LC-MS/MS analysis in HCoV-OC43 infected cells (Supplementary Fig. 4), indicating that both viruses reduce tRNA modifications at positions that potentially affect tRNA stability and translation fidelity.

Collectively, the mim-tRNAseq analysis shows that SARS-CoV-2, but not HCoV-OC43, alters tRNA abundance in a manner consistent with viral translational requirements and IFN activation mediated by SLFN11 and SLFN12. Importantly, the tRNAs exhibiting altered expression levels are distinct from those displaying modification changes, indicating that abundance and modification are regulated through separate mechanisms.

Methods
Cell lines
MRC5 (kindly provided by Wolfram Brune), A549 (kindly provided by Ana Janic), and Calu3 (kindly provided by Alfredo Castello) cells were grown at 37 °C and 5% CO2. MRC5 and A549 cells were cultivated in Dulbecco’s modified Eagle’s medium (DMEM, Thermo Fisher, 41966-029) supplemented with 10% fetal bovine serum (FBS, Sigma F7524), 1% non-essential amino acids (NEAA, Gibco 11140-035) and 1% Penicillin/Streptomycin 10.000 U/ml (Pen/Strep, Thermo Fischer 15140163). Calu3 cells were cultivated in DMEM supplemented with 20% FBS and 1% Pen/Strep.

Viruses and infection conditions
BHK21 (kindly provided by Andres Merits) and VERO-E6 (ATCC CRL-1587) cells were used to produce viral stocks. BHK21 cells were grown at 33 °C and 5% CO2 and cultivated in Glasgow Minimum essential medium (GMEM, Lonza BE12-739F), supplemented with 10% FBS, 10% triptose phosphate buffer and 1M Hepes pH 7.2 (Gibco 15630080). VERO-E6 cells were grown at 37 °C and 5% CO2 and cultivated in DMEM supplemented with 10 % FBS, 1% NEAA and 1% Pen/Strep. Stocks of the SARS-CoV-2 strain hCoV-19/Spain/VH000001133/2020 (EPI_ISL_418860) (kindly provided by Miguel Chillon Rodriguez) were generated in VERO-E6 cells and titrated by qPCR assay. HCoV-OC43 was purchased at ATCC (VR-1558TM) and was propagated in BHK21 cell lines and titrated in the same cell line by TCID50. Titers were obtained four days post-infection and calculated using the Spearman-Kärber formula. SARS-CoV-2 infections were carried out in Calu3 cells. Briefly, cells were seeded and the day after were washed twice with phosphate-buffered saline (PBS, Cultek SH30028.02) and incubated with SARS-CoV-2 at an MOI of 3 for 1 h in DMEM containing 1% FBS. Plates were shaken every 10 minutes (min). After incubation, the virus was removed, and pre-warmed DMEM + 10% FBS was added. HCoV-OC43 infection was carried out in MRC5 cells and A549 cells. Briefly, cells were seeded and the day after were washed twice with PBS and incubated with HCoV-OC43 at an MOI of 0.1 for 2 h in DMEM containing 1% FBS. Infection was carried out at 37 °C for MRC5 cells and 33 °C for A549 cells. Cells were harvested and analyzed at the indicated time points. Golden Syrian Hamsters were intranasally infected with the ancestral SARS-CoV-2 carrying the D614G mutation (viral load 104) in spike (n=6) as described previously, and lungs were collected 2 days post-infection. Uninfected hamsters were used as controls (n=6)64. Tissues were harvested and immediately homogenized in TRIzol (Thermo Fisher Scientific 15596018). Phase separation was achieved by adding 200 µL of chloroform per 1 mL of TRIzol, shaking vigorously for 15 seconds, and incubating at RT for 2 min. Samples were centrifuged at 12,000 × g for 15 min at 4 °C, and the aqueous phase was transferred to new tubes. RNA was precipitated by adding 500 µL of isopropanol per 1 mL of TRIzol, incubating for 10 min at RT, followed by centrifugation at 12,000 × g for 10 min at 4 °C. The RNA pellet was washed with 75% RNase-free ethanol (1 mL per 1 mL of TRIzol), centrifuged at 7500 × g for 5 min at 4 °C, and air-dried for 5–10 min. Pellets were resuspended in RNase-free water and incubated at 55–60 °C for 10 min. RNA was either stored at −80 °C or immediately processed for DNase treatment with TURBO DNase (Thermo Fisher Scientifi AM1907c). Reactions were incubated at 37 °C for 1 h, followed by DNase Inactivation for 5 min at 37 °C. Samples were centrifuged at 10.500 × g for 1.5 min, and the supernatant containing RNA was transferred to new tubes. RNA concentration was measured using a Nanodrop spectrophotometer.

Immunodetection
For immunoblot analysis of cell lysates, cells were lysed at the indicated timepoints in boiling 2x SDS-PAGE sample buffer (125 mM Tris-HCl, pH 6.8, 4% SDS, 20% glycerol, 10% β-mercaptoethanol) and incubated 10 min at 95 °C. Samples were separated on denaturing SDS-PAGE gels and transferred electrophoretically onto nitrocellulose (trans-blot turbo, BioRad 12023835) by turbo-blotting (trans-blot turbo transfer system, BioRad 1704150EDU). Antibodies recognizing the following epitopes and proteins were used: SARS-CoV-2 NSP8 (GeneTex GTX632696, 1:1000); KIAA1456 (Thermo Fisher PA4-70320, 1:500); ALKBH8 (AV41106 Sigma-Aldrich AV41106, 1:1000); ADAT2 (mybiosurce mbs2621771, 1:1000); ALKBH1 (Abcam ab126596, 1:1000); NSUN2 (Proteintech 20854-1-AP, 1:5000); QTRT1 (D7) (Santa Cruz Biotechnology sc398918, 1:1000); Vinculin (Sigma-Aldrich V9131, 1:1000); GAPDH (Proteintech 60004-1-IG, 1:10000); Phospho-histone H2AX (Ser139) (Cell Signaling 9718S, 1:1000); ATM (D2E2) (Cell Signaling 2873S, 1:1000); Phospho-ATM (S1981) (Cell Signaling 13050S, 1:1000); ATR (Cell Signaling 2790S, 1:1000); Phospho-ATR (S428) (Cell Signaling 2853S, 1:1000); Phospho-p38 MAPK (Thr180/Tyr182) (Cell Signaling 9215T, 1:1000); SLFN11 (E4) (Santa Cruz Biotechnology sc374339, 1:1000); SLFN12 (Abmart X-C9J4K7-N, 1:1000); HCoV-OC43 Nucleocapsid Antibody (NP) (Sino Biological-abyntech 40643-T62, 1:20000); ELP3 (D5H12) (Cell Signaling 5728, 1:1000). For detection, HRP-conjugated secondary antibodies (goat anti-rabbit IgG-HRP or goat anti-mouse IgG-HRP, Jackson ImmunoResearch GENA934 and GENA931, 1:5000) were used in combination with chemiluminescent substrate (Cytiva 10600119) for standard immunoblotting. Alternatively, fluorescent secondary antibodies (StarBright Blue 700, Bio-Rad 12004161, 1:2500) were employed, and signals were visualized using a ChemiDoc MP Imaging System (Bio-Rad). Uncropped and unprocessed western blot scans are provided in the Supplementary Information File.

RNA extraction and analysis
Extracellular RNA was extracted from 400 μL supernatant using ZR small RNA PAGE Recovery kit (Zymo Research R1070) following the manufacturer’s instructions. Intracellular RNA was extracted from cells by phenol-chloropropane extraction, after cell lysis with a buffer containing 10 mM Tris-HCl pH 7.5, 10 mM MgCl2, 100 mM NaCl, 10% Triton, and 5% DTT 0.1 M. Lysates were incubated 10 min at 60 °C with proteinase K (NEB), phenol-chloropropane extracted and precipitated with ethanol. The pellet was resuspended in RNAse-free water and treated with 0.1 μl TURBO DNase (Thermo Fisher Scientifi AM1907c) for 30 min at 37 °C. Viral RNA was analyzed by qPCR using qScript XLT One-Step RT-qPCR ToughMix (Quanta BioSciences 733-2232) using 18 ng of total RNA. RNA were reverse transcribed and amplified using gene-specific primers and TaqMan probes for all analyzed genes. The following primers for HCoV-OC43 N-protein were used: Forward TCAGCGTGGTCATAAGAATGG; Reverse GTGTCTTCAGTATAGGGCTCATC; Probe 56-FAM/ATAAGTGTTGCAGTGCCCAAAAGCC/36-TAMSp. The following primers for SARS-CoV-2 N-protein were used: Forward GACCCCAAAATCAGCGAAAT; Reverse TCTGGTTACTGCCAGTTGAATCTG; Probe 56-FAM/ACCCCGCATTACGTTTGGTGGACC/BHQ1.

For cellular genes, 500 ng of total RNA was initially retro-transcribed using the SuperScript™ III One-Step RT-PCR System with Platinum™ Taq DNA Polymerase (Thermo Fisher 12574026) with random primers (Thermo Fisher N8080127). Subsequently, quantitative real-time polymerase chain reaction (RT-qPCR) was performed using the PowerTrack™ SYBR Green Kit (Thermo Fisher A46012). The following primers were used: IRF3 Forward TCTGCCCTCAACCGCAAA GAAG; IRF3 Reverse TACTGCCTCCACCATTGGTGTC; MX1 Forward GGCTGTTTACCAGACTCCGACA; MX1 Reverse CACAAAGCCTGG CAGCTCTCTA; ISG15 Forward CTCTGAGCATCCTGGTGAGGAAI; ISG15 Reverse AAGGTCAGCCAGAACAGGTCGT; OAS1 Forward AGGAAAGGTGCTTCCGAGGTAG; OAS1 Reverse GGACTGAGGAAGA CAACCAGGT; IFNA1 Forward AGAAGGCTCCAGCCATCTCTGT; IFNA1 Reverse TGCTGGTAGAGTTCGGTGCAGA; IFNB1 Forward CTTGGATTCCTACAAAGAAGCAGC; IFNB1 Reverse TCCTCCTTCTGGAACTGCTGCA; GAPDH Forward TGTCAGTGGTG GACCTGAC; GAPDH Reverse GTGGTCGTTGAGGGCAATG. qPCR were executed in the QuantStudio™ 3 real-time PCR system from (Thermo Fisher A28567). The relative changes in gene expression were determined using the 2-ΔCt (cycle threshold) quantification method, normalized to the expression levels of GAPDH. A significance test was conducted using a Student's t test with GraphPad Prism v10.

siRNA-mediated knockdown and plasmid overexpression
A549 cells were transfected with 5 pmol siRNA-SMARTpool toward ELP3 (Cultek L-015940-01-0005) and QTRT1 (Cultek L-007081-01-0005) using Lipofectamine RNAiMAX (Thermo Fisher 13778030) for 24 h, followed by infection with HCoV-OC43 at an MOI of 0.1. At 2 hpi, the inoculum was removed, and cells were subjected to a second round of siRNA transfection. Cells, supernatants, and protein lysates were harvested at 48 hpi for downstream analyses. A non-targeting siRNA pool (scr) was used as a control (Cultek D-001810-10-05). For overexpression, cells were transfected with 500 ng plasmid DNA encoding ALKBH1-myc-ddk (Origene SKURC206529), ADAT2-myc-ddk (Origene SKURC236014), or an empty pcDNA vector for 24 h, followed by infection with HCoV-OC43 (MOI 0.1). At 2 hpi, the input virus was removed, and cells were re-transfected with the respective plasmid. FuGENE HD (FuGENE Lab clinics HD-1000) was used as a transfection reagent in a ratio of 1:4. Protein, RNA, and supernatant samples were collected at 48 hpi for analysis.

DCFH-DA assay
Intracellular ROS levels were measured using the fluorescent probe 2′,7′-dichlorodihydrofluorescein diacetate (DCFH-DA Sigma-Aldrich D6883). A549 cells were infected with HCoV-OC43 at an MOI of 0.1 and incubated for 48 h at 33 °C. Cells were washed twice with PBS and incubated with 10 µM DCFH-DA in serum- and phenol-free medium (DMEM, high glucose, HEPES, no phenol red, Gibco 11520556) for 30 min at 37 °C in the dark. After staining, cells were washed, detached with phenol-free trypsin, and immediately analyzed by flow cytometry using a BigFoot Spectral Cell Sorter (Thermo Fisher Scientific). Fluorescence was collected in the FITC channel (488), and ROS levels were quantified by calculating the mean fluorescence intensity (MFI, CT mean) of the cell population using FlowJo software (BD Biosciences). For positive control treatment, cells were incubated with 1 mM H₂O₂ for 30 min prior to DCFH-DA staining.

ATM inhibitor treatment
A549 cells were pretreated with the ATM inhibitor CP-466722 (5 µM, MedChemExpress HY-11002) for 1 h prior to infection. Cells were then infected with HCoV-OC43 at an MOI of 0.1 for 2 h at 33 °C. Following removal of the inoculum, cells were replenished with fresh medium containing CP-466722 (10 µM), which was maintained for the duration of the experiment. At 48 hpi, cells were harvested, and protein lysates were prepared for Western blot analysis.

tRNA extraction and analysis by LC-MS/MS
The tRNA fraction was isolated from 5 up to 10 µg of total RNA. tRNAs were separated using 10 or 15% Novex TBE-Urea gels (Novex, Thermo Fisher Scientific EC6875BOX or EC6885BOX). The RNA was mixed with the same amount of Novex TBE-UREA sample Prep Buffer (2x) (Novex LC6876), loaded into the gels and run at 180 V for 60 min in 1X Novex TBE Running Buffer (Invitrogen LC6675). Gels were stained with 1:5000 Sybr Gold (Thermo Fisher Scientific S11494), and the tRNA bands were excised. tRNA was then extracted using the ZR small RNA PAGE Recovery kit (Zymo Research R1070) following the manufacturer’s instructions. 150 ng of the obtained tRNA was digested with 1 µL Nucleoside Digestion Mix (New England BioLabs M0649S).

Chromatographic and mass spectrometric analysis
Samples (15 ng) were analyzed using an Orbitrap Eclipse Tribrid mass spectrometer (Thermo Fisher Scientific, San Jose, CA, USA) coupled to an EASY-nLC 1200 (Thermo Fisher Scientific (Proxeon), Odense, Denmark). Ribonucleosides were loaded directly onto the analytical column and were separated by reversed-phase chromatography using a 50 cm column with an inner diameter of 75 μm, packed with 2 μm C18 particles (Thermo Fisher Scientific, cat # ES903). Chromatographic gradients started at 93% buffer A and 3% buffer B with a flow rate of 250 nl/min for 5 min and gradually increased to 30% buffer B and 70% buffer A in 20 min. After each analysis, the column was washed for 10 min with 0% buffer A and 100% buffer B. Buffer A: 0.1% formic acid in water. Buffer B: 0.1% formic acid in 80% acetonitrile. The mass spectrometer was operated in positive ionization mode with nanospray voltage set at 2.4 kV and source temperature at 305 °C. For the Parallel Reaction Monitoring (PRM) method, the quadrupole isolation window was set to 1.4 m/z, and MSMS scans were collected over a mass range of m/z 50–300, with detection in the Orbitrap at a resolution of 60,000. MSMS fragmentation of defined mases at scheduled retention time (Supplementary Data 1) was performed using HCD at NCE 20 (except stated differently, Supplementary Data 1)65, the auto gain control (AGC) was set to “Standard” and a maximum injection time of 118 ms was used. In each PRM cycle, one full MS scan at a resolution of 120,000 was acquired over a mass range of m/z 220-700 with detection in the Orbitrap mass analyzer. Autogain control (AGC) was set to 1e5, and the maximum injection time was set to 50 ms. Serial dilutions were prepared using commercial pure ribonucleosides (0.005-150 pg, Carbosynth, Toronto Research Chemicals) in order to establish the linear range of quantification and the limit of detection of each compound. A mix of commercial ribonucleosides was injected before and after each batch of samples to asses instrument stability and to be used as an external standard to calibrate the retention time of each ribonucleoside (Supplementary Data 1).

LC-MS/MS Data analysis
Acquired data were analyzed with the Skyline-daily software (v24.1.1.284), and extracted precursor areas of the ribonucleosides were used for quantification (Source Data File).

Ribosome profiling data processing
We re-analyzed publicly available ribosome profiling and RNA-seq datasets of SARS-CoV-2 infection in Calu3 cells at 36 h post-infection (hpi) from NCBI GEO (accession number GSE157490)43. Adapters were trimmed using cutadapt (v4.2), retaining reads ≥ 50 nt for RNA-seq and 25–35 nt for Ribo-seq. Reads mapping to non-coding RNAs (tRNAs, rRNAs, and other ncRNAs) were removed by alignment with bowtie2 (v2.4.5). For human transcripts, we used the GENCODE v48 annotation (GRCh38.p14, GCA_000001405.29) and restricted the analysis to the MANE Select transcript isoforms, as defined by the Appris database (appris_data.principal_mane_match_G48.tsv). The SARS-CoV-2 reference genome and transcriptome were obtained from GenBank (NC_045512.2) and combined with the human annotation. Processed reads were aligned to this merged reference using STAR (v2.7.10b). RNA-seq counts were quantified with featureCounts (subread v1.5.1). Assignment of A- and P-sites and ribosome footprint counting were performed using riboWaltz (v2.0), considering footprints of length 29–33 nt and determining offsets with the “extremity=auto” parameter. Differential expression analyses were performed using DESeq2 using two-sided Wald tests66 for RNA-seq, and Ribo-seq counts independently. Translation efficiency was calculated using RiboDiff67. Genes were classified as translationally activated (TA), translationally repressed (TR), or not significantly changed (NS). Codon usage was computed using the uco function from the seqinR package (v4.2-36). Occurrences of G-, C-, A-, and U-ending codons were determined for each transcript, and relative usage of codons recognized by inosine (I)-tRNAs, queuosine (Q)-tRNAs, and mcm5U-tRNAs was calculated. For Q-family codons, the percentage of each individual NNC and NNU codon was calculated as its mean occurrence in the corresponding gene set divided by the total occurrence of all Q-family codons (Supplementary Data 2). For every sample, ribosome occupancy density of each gene was calculated by using riboWaltz inbuilt function codon_usage_psite and normalized to the total counts of RPFs per gene68. Then, the average codon occupancy in SARS-infected conditions was divided by non-infected conditions for codons associated with tRNA modifications. Results were visualized using ggplot.

mim-tRNAseq
The analysis of tRNA expression was performed using the mim-tRNAseq protocol as described45,46. Briefly, total RNA was extracted from Calu3 cells infected with SARS-CoV-2 and MRC5 cells infected with HCoV-OC43 using phenol-chloroform extraction, followed by precipitation with ethanol. RNA was deacylated using Tris-HCl (pH 9), followed by dephosphorylation with T4 PNK (NEB M0201S). Next, tRNA was purified on a 10% TBE-Urea gel (Novex, Thermo Fisher Scientific EC6875BOX), size-selected (60–100 nt), and eluted from the gel using the ZR Small RNA PAGE Recovery Kit (Zymo Research R1070) according to the manufacturer’s instructions. Preadenylated barcoded 3’ adapter oligonucleotides (containing a 5’ phosphate and blocked with a 3’ dideoxycytidine) were ligated to the purified tRNA using T4 RNA ligase 2 truncated KQ (NEB M0373L), in a reaction containing 50% PEG-8000, T4 RNA ligase buffer (10X), and RNase inhibitor (SUPERase•In, Invitrogen AM2694) for 3 hours at 25 °C. The following barcoded 3’ adapters were used (sequences provided 5’ to 3’): pGATATCGTCAAGATCGGAAGAGCACACGTCTGAAddC; pGATAGCTACAAGATCGGAAGAGCACACGTCTGAAddC; pGATGCATACAAGATCGGAAGAGCACACGTCTGAAddC; pGATTCTAGCAAGATCGGAAGAGCACACGTCTGAAddC. Reaction products with different barcoded 3’ adapters were pooled and purified with the Oligo Clean & Concentrator Kit (Zymo Research D4060) before size selection by gel (110–125 nt), as described above. A total of 100 ng of adapter-ligated tRNA was used for a primer-dependent reverse transcription reaction containing 125 nM RT primer (5’-pRNAGATCGGAAGAGCGTCGTGTAGGGAAAGAG/iSp18/GTGACTGGAGTTCAGACGTGTGTGCTC-3’) previously annealed at 82 °C for 2 min 500 nM TGIRT (InGex) in FS 5X buffer (SuperScript III, Thermo Fisher 18080085), freshly prepared DTT (0.5 mM), RNase inhibitor, and dNTPs (25 mM), and incubated at 42 °C for 16 h. After reverse transcription, 1 μl of 5 M NaOH was added, and the RNA was hydrolyzed for 5 min at 95 °C. cDNA products were circularized using CircLigase ssDNA Ligase (Lucigen CL4111K) in a reaction containing CircLigase buffer (10X), ATP (1 mM), MnCl₂ (50 mM), and betaine (5 M) for 3 h at 60 °C. The circularized cDNA was used for library construction PCR using KAPA HiFi DNA Polymerase (Roche, KK2101) in KAPA HiFi GC buffer (5X) with a common forward primer (5’ AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCT*C-3’) and specific indexed reverse primers with the reverse complement of an Illumina index sequence (sequences provided 5’ to 3’): CAAGCAGAAGACGGCATACGAGATTGACCAGTGAC TGGAGTTCAGACGTGT*G; CAAGCAGAAGACGGCATACGAGATACAGT GGTGACTGGAGTTCAGACGTGT*G; CAAGCAGAAGACGGCATACGAGA TGCCAATGTGACTGGAGTTCAGACGTGT*G. Amplified libraries were purified using the DNA Clean & Concentrator Kit (Zymo Research D4013), pooled, and purified by size selection on an 8% non-denaturing polyacrylamide gel (150–230 bp). Libraries were quantified with real-time PCR and sequenced on an Illumina NextSeq 500 platform with a single-end run of 150 cycles. Read demultiplexing and adapter trimming were performed with Cutadapt (v3.7). Both read ends were quality-trimmed (-q 30, 30), and reads shorter than 10 nt were discarded (-m 10). Indels were not allowed (--no-indels), and only trimmed reads were retained (--trimmed-only). An additional round of trimming was performed to remove two extra 5’ nucleotides introduced by cDNA circularization (-u 2). tRNA expression and modification analysis were processed using the mim-tRNAseq computational pipeline (v1.3.8) with the following parameters: --species Hsap --cluster-id 0.97 --threads 5 --min-cov 0.0005 --max-mismatches 0.1 --max-multi 4 --remap --remap-mismatches 0.075.

Relative synonymous codon usage analysis (RSCU)
Viral coding sequences were retrieved from the NCBI GenBank. Only high-quality, annotated CDS were included in the analysis. Redundant sequences, pseudogenes, and incomplete coding regions were excluded to ensure accurate codon frequency calculations. For Alphacoronaviruses and Betacoronaviruses, coronavirus genes included ORF1ab, spike, envelope, membrane protein, and nucleocapsid genes, concatenating these sequences in the aforementioned order. Genes such as 3a/b, 4, and 7b were excluded from the study due to annotation inconsistencies. The human CDS genome was derived from a custom reference described10. Briefly, the hg38 human genome sequence was from the USCS Genome Browser and the most highly expressed protein-coding splice isoform in HEK293T RNA-seq data were selected using Appris principal isoforms annotation based on GENCODE v24. The sequence of each transcript was concatenated and RSCU was calculated via the “uco” function from the seqinR package69. RSCU values of less than 0.9 are considered underrepresented codons and RSCU values above 1.1 are deemed overrepresented codons. The R code utilized in this study is available from the corresponding author upon request.

Large language models (LLMs) usage
During the preparation of this manuscript, ChatGPT was utilized to assist with grammatical corrections. All content was reviewed and approved by the authors to ensure accuracy and integrity.

Ethics and Inclusion statement
Procedures involving animals were performed under UK Home Office License PP0271643 in accordance with the Animals (Scientific Procedures) Act 1986 and approved by the University of Glasgow Ethics Committee. All animal research adhered to ARRIVE guidelines70.

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

Data availability
The minimum dataset that supports the findings of this study is provided in this paper. The western blot scans generated in this study are included in the Supplementary Information. The data presented in graphs within the figures are provided in the Source Data File. The ribosome profiling analyses are provided in Supplementary Data 2. The mim-tRNA-seq sequencing data have been deposited in BioProject under the accession number PRJNA1262447. The mass spectrometry raw data have been deposited in MetaboLights under the accession number MTBLS1373871. Source data are provided in this paper.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In their manuscript “Coronaviruses reprogram the tRNA epitranscriptome to favor viral protein expression.” The authors demonstrate that coronavirus genomes are enriched with suboptimal A- and U-ending codons. They propose that this bias reflects an adaptation of viral codon usage to the host tRNA modification landscape under stress conditions induced by infection. The observations and underlying hypothesis are intriguing and may exemplify how the viral transcriptome has coevolved with the host tRNA modification machinery to optimize viral translation efficiency. However, the manuscript relies entirely on observational data and correlations, lacking direct cause-and-effect evidences and experimental validation. This limitation significantly weakens the strength and overall impact of the study.\nTo validate their hypothesis, the authors should consider manipulating the host cell tRNA modification machinery, using host cells that either lack or overexpress the tRNA modification enzymes implicated in viral adaptation. One might expect reduced viral protein expression in knockout cells deficient in specific enzymes, such as Qtrt1/Qtrt2, as well as in cells overexpressing ADAT2. The same principle applies to NSun2 and ALKBH8.\nIn addition, I have the following major issues:\nMass spectrometry reveals variations in overall modifications upon infection but fails to demonstrate site-specific changes. While some observed trends align with the author’s hypothesis, it remains unclear whether the changes are occurring on the expected tRNAs at position N34 and whether they result from an increase or decrease in modifications at these sites. The observed changes may be due to variations in tRNA expression, as suggested in Figure 5, which would inevitably affect the modification-to-tRNA ratio.\nTo validate conclusions regarding changes in tRNA modifications upon viral infection, a modification/tRNA-targeted approach is necessary.\nMoreover, since mitochondrial tRNA modifications should not directly affect viral protein translation, their contribution, along with mit-tRNA expression, to the overall observed tRNA modification changes remains unclear.\nEven if some modification derivatives are evaluated and discussed, others like Q/manQ/galQ (PMID: 37992713) are completely ignored, especially in their impact on the viral codon translation.\nMinor\nPage 4 row 136 the statement “ When Q-modified, G34, which normally pairs with C or with near‐cognate Q‐decoded codons (such as Glu-GAA and Glu-GAG ), can also base pair with U, disfavoring decoding of coronavirus codons” is in contradiction with Figure 1F. Please clarify.", "reviewer_2": "In this study, authors found that SARS-CoV-2 and HCoV-OC43 infection induces extensive reprogramming of inosine, queuosine, mcm5U/mcm5s2U and m5C/f5C tRNA modifications. It is an interesting study that contributes to our understanding of host-virus interactions, particularly how viruses can exploit epitranscriptomic mechanisms to improve viral protein expression. However, there are several specific concerns and results interpretations that need to be addressed to support stated conclusions and strengthen the significance of these findings.\nMajor Comments\n1. The authors used p-p38 to analyze oxidative stress. The activation of p38 MAPK is involved in various biological processes. Therefore, p-p38 is not an ideal biomarker of oxidative stress. The authors should provide ROS or MDA analysis data to confirm the oxidative stress.\n2. The authors used MOI 3 for SARS-CoV-2 infection, MOI 0.1 for OC43 infection. Why use such a high MOI for SARS-CoV-2? Are similar tRNA changes observed at MOI 0.1? If yes, please provide the results from low MOI to higher MOI conditions. Were these changes only observed under high MOI conditions? If yes, why?\n3. In figure 3, authors showed the up/downregulation of inosine, queuosine, mcm5U, and f5C in cells. However, only the increase of mcm5U was further confirmed in vivo. The other tRNA changes also need to be confirmed in vivo.\n4. In figure3, the OC43 infection induced more extensive tRNA modification changes than SARS-CoV-2. However, in figure5, the host tRNA abundance only was observed in SARS-COV-2 infection but not in OC43 infection. Is it reasonable for such a phenomenon to occur? Please clarify this point. And the title “Coronaviruses alter the host tRNA abundance” of this section should be changed.\n5. In Figure 6, it is unclear whether the same MOIs were used as in previous experiments—MOI 3 for SARS-CoV-2 and MOI 0.1 for OC43. If this is the case, the observed differences in SLFN11/12 expression and interferon-related genes expression between the two viruses may be influenced. The authors should clarify the MOIs used in this figure and discuss how differences in viral dose may impact the interpretation of host response data.\nMinor Comments\n1. The title claims that coronaviruses reprogram the tRNA epitranscriptome to favor viral protein expression. Therefore, the increase of viral protein levels is a critical phenomenon. The authors should show viral protein expression levels in main figures not just in supplementary figures. Extended figure 2 showed only SARS-COV-2 protein levels. The authors should add OC43 viral protein expression data.\n2. The authors propose that SLFN11/12 upregulation leads to a decrease in type 2 tRNA abundance under SARS-COV-2 infection. However, the observed timing is inconsistent: the reduction in type 2 tRNAs occurs at 32 hours post-infection, whereas SLFN11/12 expression increases later, at 48 hours. This temporal discrepancy raises questions about whether SLFN11/12 induction is the cause of the tRNA reduction or a downstream consequence. Clarification of this timeline, or additional data supporting a delayed effect of SLFN11/12, would strengthen the authors' conclusion.\n3. Add a graphical abstract or summary figure may help readers grasp the key findings.", "reviewer_3": "Coronavirus genomes are enriched in suboptimal codons, which potentially reduce the rate of protein synthesis. However, coronavirus proteins can be efficiently translated despite the codon usage conflict. To tackle this inconsistency, the authors uncovered that coronavirus infection changes tRNA-modifying enzyme expression and tRNA epi-transcriptomics to modulate specific codon optimality. This manuscript shows the interesting trends of tRNA modification changes and the corresponding enzyme expression. However, there are still many points that should be addressed before the publication.\nMajor comments:\n1. The authors show attractive results in each figure, including tRNA epi-transcriptome changes. However, the relationships between each finding and viral expression are missing. Authors should perform ribosome profiling of virus-infected samples to investigate whether or not these RNA modification changes associate with smooth ribosome elongation on virus mRNAs and host mRNAs.\n2. To investigate whether DNA damage response contributes to changes in tRNA modification under viral infections, authors should check whether artificially induced DNA damage by chemicals (without viral infection) shows reproducible epi-transcriptome results similar to viral infections, and confirm that DNA damage inhibitors counteract the tRNA modifications by LC-MS/MS and tRNA-seq.\n3. To uncover the relationship between upregulated expression of KIAA1456/ALKBH8, mcm5U modification, codon optimality, and viral protein expression under viral infection, the authors should perform KIAA1456/ALKBH8 knockdown, check mcm5U modification, viral protein expression changes by Western blotting, and calculate ribosome occupancy across codons by ribosome profiling.\n4. Similarly, to check the contribution of SLFN11/12 to reducing specific tRNA abundance, the authors should perform SLFN11/12 knockdown, check tRNA abundance, and perform ribosome profiling to calculate ribosome occupancy on target codons.\n5. To confirm the relationship between oxidative stress, tRNA modification, and codon optimality, the authors should chemically induce oxidative stress, examine its effects on enzyme expression regulation and changes in tRNA modifications such as queuosine and f5C, and check the codon optimality by ribosome profiling.\n6. The authors suggested that the protein level of the corresponding tRNA modification enzymes was changed upon viral infection and the associated tRNA modification per se. This reviewer guessed that the enzyme expression comes first, but the mechanism was not studied. The authors should reveal the molecular mechanisms.\nMinor comments:\n1. Some Western blot results are ambiguous; the authors should repeat the blotting experiments. Figure 2B: pATM, ATM, and pATR bands are unclear, and GAPDH intensity appears to be saturated. Figure 6A: SLFN11/12 intensity appears to be saturated. The quantification of Western blots from triplicate (or more) would be helpful. Additionally, this reviewer wondered why there are two GAPDH bands in Figure 6A/B. Please explain the details.\n2. The authors should check whether DDR inhibitors and/or oxidative stress inhibitors downregulate MAPK phosphorylation under viral infection.\n3. Homo sapiens should be italicized in Figure 1 legends and Extended Data 1 legends.\n4. The text in the heatmap of Extended Data 6 is too small and needs adjustment for readability.\n5. The conclusion sentence in lines 172-174 was too strong from the data. The authors should tone down the nuance.\n6. The sentence in lines 214-217, the authors explained that f5C was found in mt-tRNAs. This was indeed true for Q. This point should be highlighted.\n7. The authors should discuss manQ and galQ as well.", "reviewer_4": "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.", "reviewer_5": "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": "POINT BY POINT RESPONSE\nReviewer #1 (Remarks to the Author):\nIn their manuscript “Coronaviruses reprogram the tRNA epitranscriptome to favor viral protein expression.”\nThe authors demonstrate that coronavirus genomes are enriched with suboptimal A- and U-ending codons.\nThey propose that this bias reflects an adaptation of viral codon usage to the host tRNA modification\nlandscape under stress conditions induced by infection. The observations and underlying hypothesis are\nintriguing and may exemplify how the viral transcriptome has coevolved with the host tRNA modification\nmachinery to optimize viral translation efficiency. However, the manuscript relies entirely on observational\ndata and correlations, lacking direct cause-and-effect evidences and experimental validation.\nThis limitation significantly weakens the strength and overall impact of the study.\nMajor:\nPOINT #1\nTo validate their hypothesis, the authors should consider manipulating the host cell tRNA modification\nmachinery, using host cells that either lack or overexpress the tRNA modification enzymes implicated in viral\nadaptation. One might expect reduced viral protein expression in knockout cells deficient in specific enzymes,\nsuch as Qtrt1/Qtrt2, as well as in cells overexpressing ADAT2. The same principle applies to NSun2 and\nALKBH8.\nANSWER #1:\nTo address this key issue, we focused on HCoV-OC43 infection on A549 cells because (i) the observed changes\nin tRNA modifications were conserved across both viruses analyzed, (ii) transfection efficiency in the Calu3\nand MRC5 cells lines (used for SARS CoV-2 and HCoV-OC43 infection, respectively) is poor and (iii) A549 cells\nsupport HCoV-OC43 replication (Extended Data 3D-F) and can be efficiently transfected.\nConsistent with a functional causal link between the observed tRNA epitranscriptome alterations and\nviral protein synthesis, we found that (i) siRNA-mediated knockdown of the tRNA modifying enzymes ELP3\n(mcm5U modification pathway) and QTRT1 (queuosine modification pathway), as well as (ii) overexpression\nof ADAT2 (A-to-I modification enzyme) and ALKBH1 (f5C pathway), each significantly reduced viral protein\nlevels (N-protein). In contrast, NSUN2 silencing had no detectable effect. The lack of an effect upon NSUN2\ndepletion, combined with the observed downregulation of ALKBH1 during infection and the strong inhibitory\neffect on viral N-protein expression upon its overexpression, suggests that the virus primarily modulates the\nNSUN3–ALKBH1 mitochondrial pathway, rather than the cytosolic NSUN2-dependent m⁵C modification. This\ninterpretation is further supported by the lack of detectable changes in global m⁵C tRNA modification levels\nduring infection (Figure 3A).\nWe silenced ELP3 instead of KIAA1456 or ALKBH8 because it acts upstream in the mcm⁵ modification\npathway, directly influencing the activity of these enzymes, and because ELP3 expression itself was strongly\nupregulated during HCoV-OC43 infection (Figure 4A).\nTogether, these findings demonstrate that the observed alterations of the tRNA-modifying enzymes under\ninfection conditions functionally contribute to coronavirus viral protein production. These results are now\nincluded in the new Figure 4 of the revised manuscript. The following text has been added at lines 273 - 291:\n“To establish a causal link between the altera2on of these enzymes and viral protein expression, we\nmanipulated the expression of selected tRNA modifying enzymes. These experiments were performed using\nHCoV-OC43 infec2on in A549 cells because the observed changes in tRNA modifica2ons were conserved across\nboth viruses analyzed, transfec2on efficiency in Calu3 and MRC5 cell lines (used for SARS-CoV-2 and HCoV-\nOC43 infec2on, respec2vely) is poor, and A549 cells support efficient HCoV-OC43 replica2on and transfec2on\n(Extended Data Figure 3D-F). Consistent with a func2onal link between tRNA epitranscriptome remodeling\nand viral protein produc2on, siRNA-mediated knockdown of ELP3 (mcm⁵U modifica2on pathway) and QTRT1\n(queuosine modifica2on pathway) significantly reduced viral protein levels (Figure 4A–B). ELP3 was selected\nfor silencing because it acts upstream of KIAA1456 and ALKBH8 in the mcm⁵U modifica2on pathway and is\nstrongly upregulated during HCoV-OC43 infec2on (Figure 4A). Similarly, overexpression of ADAT2 (A-to-I\nmodifica2on enzyme) or ALKBH1 (f⁵C pathway) also led to a pronounced decrease in viral protein expression\n(Figure 4D). In contrast, NSUN2 silencing had no detectable effect (Figure 4C). This lack of effect, together\nwith the observed downregula2on of ALKBH1 during infec2on (Figure 3H) and the strong inhibi2on of viral\nprotein produc2on upon its overexpression, suggests that viral infec2on primarily modulates the\nmitochondrial NSUN3–ALKBH1 pathway rather than the cytosolic NSUN2-dependent m⁵C modifica2on. This\ninterpreta2on is further supported by the absence of significant changes in global tRNA m⁵C levels analyzed\nby LC-MS/MS during infec2on (Figure 3A). Together, these findings demonstrate that altera2ons in mcm⁵U, Q\nand I tRNA-modifying enzymes during infec2on not only align with viral codon usage but also func2onally\ncontribute to coronavirus protein produc2on.”\nPOINT #2\nMass spectrometry reveals variations in overall modifications upon infection but fails to demonstrate site-\nspecific changes. While some observed trends align with the author’s hypothesis, it remains unclear whether\nthe changes are occurring on the expected tRNAs at position N34 and whether they result from an increase\nor decrease in modifications at these sites. The observed changes may be due to variations in tRNA\nexpression, as suggested in Figure 5, which would inevitably affect the modification-to-tRNA ratio. To validate\nconclusions regarding changes in tRNA modifications upon viral infection, a modification/tRNA-targeted\napproach is necessary.\nANSWER #2:\nWe thank the reviewer for the comment and apologize for any lack of clarity in our initial text. We agree that\ncertain tRNA modifications can occur at multiple positions. However,f mcm⁵U/mcm⁵s²U, queuosine (Q),\ninosine (I), and f⁵C modification analyzed in our study are well established to occur exclusively at position\nN34 of their cognate tRNAs, as described in Suzuki T., 2021 (See Reference 1 listed at the end of this\nresponse). The only exception was m⁵C that can occur at multiple positions. Moreover, to exclude the\npossibility that the observed differences resulted from altered tRNA expression rather than modification\nlevels, we performed mim-tRNAseq analysis. Differential expression analysis (now Figure 6A–F) revealed no\nsignificant changes in the abundance of tRNAs carrying these modifications. We have now adjusted the\nsentence at lines 128 – 130:\n“Indeed, the decoding of 48% of codons enriched in coronavirus genomes is affected by four tRNA\nmodifications: I, Q, mcm5/mcm5s2U, which occur exclusively at position N34, and m5C, which can also occur\nat other tRNA positions besides N34”.\nMoreover, we have included a final sentence at lines 364–366 to further clarify this point.\n“Importantly, the tRNAs exhibiting altered expression levels are distinct from those displaying modification\nchanges, indicating that abundance and modification are regulated through separate mechanisms”.\n1. Suzuki, T. The expanding world of tRNA modifications and their disease relevance. Nat Rev Mol Cell Biol 22,\n375–392 (2021). https://doi.org/10.1038/s41580-021-00342-0).\nPOINT #3\nMoreover, since mitochondrial tRNA modifications should not directly affect viral protein translation,\ntheir contribution, along with mit-tRNA expression, to the overall observed tRNA modification changes remains\nunclear.\nANSWER #3:\nWe agree with the reviewer that mitochondrial tRNA modifications are unlikely to directly influence viral\nprotein translation. In this study, we did not specifically dissect the contribution of mitochondrial tRNAs or\nmitochondrial processes. However, we note that one of the modifications altered during infection, 5-\nformylcytidine (f⁵C), occurs in both cytoplasmic (tRNA^LeuCAA) and mitochondrial (tRNA^iMet) tRNAs. Given\nthis dual localization, the changes observed in f⁵C levels may reflect infection-induced mitochondrial stress.\nSupporting this idea, as part of a new ongoing project, we have found comparable f⁵C alterations during\ninfections with viruses exhibiting optimal codon usage, suggesting that mitochondrial perturbations may\ncontribute to these modification changes independently of viral codon usage bias. We will now clarify and\ndiscuss this point in the revised manuscript by adding the sentence at lines 222-224:\n“Given that f⁵C is present in both cytoplasmic and mitochondrial tRNAs, and LC-MS/MS analysis cannot\ndiscriminate between cytosolic and mitochondrial tRNA pools, the observed f⁵C changes may reflect\nmitochondrial stress responses rather than direct effects on viral protein translation”.\nPOINT #4\nEven if some modification derivatives are evaluated and discussed, others like Q/manQ/galQ (PMID:\n37992713) are completely ignored, especially in their impact on the viral codon translation.\nANSWER #4:\nWe thank the reviewer for pointing this out. We have now included the following text in the revised\nmanuscript addressing the potential impact of manQ and galQ on viral codon translation at lines 136 – 139:\n“The Q modification and its derivatives, mannosyl-Q (manQ) and galactosyl-Q (galQ), favor the decoding of\nfour U-ending codons (Asparagine (Asn) AAU, Aspartic acid (Asp) GAU, Histidine (His) CAU, and Tyrosine (Tyr)\nUAU), with galQ found in human tRNATyr, and manQ found in human tRNAAsp (Figure 1D)”.\nMinor:\nPOINT #5\nPage 4 row 136 the statement “ When Q-modified, G34, which normally pairs with C or with near-cognate\nQ-decoded codons (such as Glu-GAA and Glu-GAG ), can also base pair with U, disfavoring decoding of\ncoronavirus codons” is in contradiction with Figure 1F. Please clarify.\nANSWER #5:\nWe thank the reviewer for noting this mistake. The wording in the manuscript was incorrect and should read\n“favoring” instead of “disfavoring.” We have corrected this in the revised version.\nReviewer #2 (Remarks to the Author):\nIn this study, authors found that SARS-CoV-2 and HCoV-OC43 infection induces extensive reprogramming of\ninosine, queuosine, mcm5U/mcm5s2U and m5C/f5C tRNA modifications. It is an interesting study that\ncontributes to our understanding of host-virus interactions, particularly how viruses can exploit\nepitranscriptome mechanisms to improve viral protein expression. However, there are several specific\nconcerns and results interpretations that need to be addressed to support stated conclusions and strengthen\nthe significance of these findings.\nMajor Comments\nPOINT #1\nThe authors used p-p38 to analyze oxidative stress. The activation of p38 MAPK is involved in various\nbiological processes. Therefore, p-p38 is not an ideal biomarker of oxidative stress. The authors should\nprovide ROS or MDA analysis data to confirm the oxidative stress.\nANSWER #1:\nWe thank the reviewer for the comment. To directly address oxidative stress, we performed a DCFH-DA assay\nto measure ROS production in HCoV-OC43 infected cells at 48 hpi compared with mock-infected controls.\nThe results show a significant increase in ROS levels upon infection. As a positive control, H₂O₂ treatment\nproduced the expected ROS induction. These new data have been included in the new Extended Data 2A-B\nof the revised manuscript and the following part has been added to the text at lines 177 -178.\n“Furthermore, ROS production was confirmed by directly measuring ROS levels in infected cell (Extended Data\n2A-B)”.\nPOINT #2\nThe authors used MOI 3 for SARS-CoV-2 infection, MOI 0.1 for OC43 infection. Why use such a high MOI for\nSARS-CoV-2? Are similar tRNA changes observed at MOI 0.1? If yes, please provide the results from low MOI\nto higher MOI conditions. Were these changes only observed under high MOI conditions? If yes, why?\nANSWER #2:\nThe MOI values were chosen based on the replication kinetics of each virus in the specific cell lines used.\nOur\ngoal was to achieve robust infection while minimizing cytotoxic effects, ensuring that observed tRNA\nmodification changes reflected biological regulation rather than cell death.\nFor SARS-CoV-2, an MOI of 3 in Calu-3 cells is commonly used in the literature and provides consistent\nreplication within 30–48 hours post-infection. Lower MOI values in this cell line result in markedly delayed\nreplication and require prolonged incubation to reach comparable infection levels, potentially introducing\nconfounding variables. In contrast, for HCoV-OC43, an MOI of 0.1 was sufficient for efficient infection, as\nhigher MOIs did not further increase viral replication, likely due to differences in entry efficiency or host cell\npermissiveness.\nPOINT #3\nIn figure 3, authors showed the up/downregulation of inosine, queuosine, mcm5U, and f5C in cells. However,\nonly the increase of mcm5U was further confirmed in vivo. The other tRNA changes also need to be confirmed\nin vivo.\nANSWER #3:\nIn our in vivo analyses, we examined the complete panel of tRNA modifications. However, only mcm⁵U\nshowed a statistically significant increase. We attribute this to the low, partial and heterogeneous infection\nlevels obtained in vivo, where it is not possible to selectively analyze highly infected regions. Consistent with\nthis, variability among samples allowed us to distinguish two subgroups based on infection levels. Despite\nthis limitation, we confirmed the functional relevance of tRNA modifications through the newly added siRNA\nknockdown and overexpression experiments in cell culture, which demonstrated clear effects on viral protein\nexpression (new Figure 4). We agree that more refined in vivo infection models could provide further\ninformation into the regulation of other tRNA modifications, and we highlight this as an important direction\nfor future studies. We have now included these sentences to clarify our in vivo findings at lines 404 – 410:\n“Upregulation of mcm5U was observed in both cell culture and in hamsters infected with the SARS-CoV-2\nancestral strain D641G, which has lower infectivity compared to the SARS-CoV-2 strain used in our in vitro\nexperiments. This upregulation was higher in samples with the highest viral load, suggesting that infection\nefficiency is a critical factor influencing the levels of tRNA modifications (Figure 3E). Taken together, these\nfindings suggest that infection efficiency influences tRNA modification levels, which might explain why the\nother tRNA modifications were not altered in vivo. Further in vivo experiments with a more suitable animal\nmodel will be required to clarify this matter”.\nPOINT #4\nIn figure3, the OC43 infection induced more extensive tRNA modification changes than SARS-CoV-2.\nHowever, in figure5, the host tRNA abundance only was observed in SARS-COV-2 infection but not in OC43\ninfection. Is it reasonable for such a phenomenon to occur? Please clarify this point. And the title\n“Coronaviruses alter the host tRNA abundance” of this section should be changed.\nANSWER #4:\nWe attribute the differences in tRNA abundance changes between SARS-CoV-2 and OC43 infections to\ndistinct host responses in the respective cell lines used. The cell line used for SARS-CoV-2 but not the one\nused OC43 infection is capable of mounting a robust type I interferon (IFN) response upon infection. IFN is\nwell established to induce SLFN11 and SLFN12 expression, two proteins known to cleave type II tRNAs\n(including tRNASer and tRNALeu), the ones we observed to be downregulated, as part of the antiviral defense\nmechanism (See References 1–5 listed at the end of this response).\nWe agree that the current section title is too general. To better reflect our key findings, we have revised it to\n“Coronaviruses reprogram modifications in pre-existing tRNAs without altering their abundance” to\nhighlight this aspect in the results.\nThe differences in tRNA modification and tRNA abundance changes between SARS-CoV-2 and OC43 infection\nare addressed at lines 342-350 and 427-430.\n“Regarding the downregulated tRNAs, all belong to the type II tRNAs, which are substrates for Schlafen 11\n(SLFN11) and Schlafen 12 (SLFN12) endonucleases44. These enzymes are induced by the interferon (IFN)\nresponse as part of the innate antiviral defense or in response to DNA damage45–48. Consistent with the\ndownregulation of type II tRNAs in SARS-CoV-2 infection, but not in HCoV-OC43 infection, SARS-CoV-2\ninfection triggered an IFN response, as indicated by the expression of IFN-stimulated genes and the strong\nupregulation of SLFN11 and SLFN12 (Extended Data 9A-C). In contrast, HCoV-OC43 did not induce a proper\nIFN response and only led to a slight activation of IFNB1 at 72 hours post-infection, likely due to the DNA\ndamage response (Extended Data 9B-D). These differences are linked to the cell lines used for SARS-CoV-2\nand HCoV-OC43 experiments”\n“The differences in tRNA modification profiles observed between SARS-CoV-2 and HCoV-OC43 infections may\nstem from the distinct cell lines used, the virus themselves, or both. SARS-CoV-2 infections were conducted in\nthe lung adenocarcinoma Calu3 cell line, whereas HCoV-OC43 infections were conducted in the lung fibroblast\nMRC5 cell line, both well-established and recommended models for their study. Cancer cell lines have been\nreported to exhibit altered levels of tRNA modifications, which may influence virus-induced tRNA modification\nprofiles”\nReferences:\n1. Kobayashi-Ishihara, M., Frazão Smutná, K., Alonso, F.E. et al. Schlafen 12 restricts HIV-1 latency reversal by a codon-usage\ndependent post-transcriptional block in CD4+ T cells. Commun Biol 6, 487 (2023). https://doi.org/10.1038/s42003-023-\n04841-y.\n2. Li, M., Kao, E., Malone, D. et al. DNA damage-induced cell death relies on SLFN11-dependent cleavage of distinct type II\ntRNAs. Nat Struct Mol Biol 25, 1047–1058 (2018). https://doi.org/10.1038/s41594-018-0142-5\n3. Metzner, F.J., Wenzl, S.J., Kugler, M. et al. Mechanistic understanding of human SLFN11. Nat Commun 13, 5464 (2022).\nhttps://doi.org/10.1038/s41467-022-33123-0\n4. Kim, E. T. & Weitzman, M. D. Schlafens Can Put Viruses to Sleep. Viruses 14, 442 (2022). https://doi.org/10.3390/v14020442\n5. Valdez F. et al. . Schlafen 11 Restricts Flavivirus Replication. J Virol 2019 93:10.1128/jvi.00104-19.\nhttps://doi.org/10.1128/jvi.00104-19\nPOINT #5\nIn Figure 6, it is unclear whether the same MOIs were used as in previous experiments—MOI 3 for SARS-CoV-\n2 and MOI 0.1 for OC43. If this is the case, the observed differences in SLFN11/12 expression and interferon-\nrelated genes expression between the two viruses may be influenced. The authors should clarify the MOIs\nused in this figure and discuss how differences in viral dose may impact the interpretation of host response\ndata.\nANSWER #5:\nThank you for pointing this out. The MOIs used in Figure 6 (now Extended Data Figure 9) were identical to\ntho\nthose applied in the previous experiments, as they were carefully optimized for each virus to ensure efficient\ninfection while preserving cell viability in the respective cell lines. We have now clarified this point in the\nfigure legend of Extended Data Figure 9. We attribute the observed differences in SLFN11/12 and interferon-\nrelated gene expression to the intrinsic responsiveness of the cell lines to IFN induction rather than to\ndifferences in viral dose.\nMinor Comments\nPOINT #6\nThe title claims that coronaviruses reprogram the tRNA epitranscriptome to favor viral protein expression.\nTherefore, the increase of viral protein levels is a critical phenomenon. The authors should show viral protein\nexpression levels in main figures not just in supplementary figures. Extended figure 2 showed only SARS-COV-\n2 protein levels. The authors should add OC43 viral protein expression data.\nANSWER #6:\nIn the revised manuscript, we have now included a new figure showing a time-course analysis of OC43 viral\nprotein expression in both MRC5 and A549 cells. Due to space constraints, these data were placed in the\nsupplementary section (Extended Data 3F). However, the effects of siRNA knockdown and overexpression\nof tRNA-modifying enzymes on viral protein expression are presented in the main figures, as these directly\nsupport our central conclusion that tRNA modifications regulate viral protein production (Figure 4).\nPOINT #7\nThe authors propose that SLFN11/12 upregulation leads to a decrease in type 2 tRNA abundance under SARS-\nCOV-2 infection. However, the observed timing is inconsistent: the reduction in type 2 tRNAs occurs at 32\nhours post-infection, whereas SLFN11/12 expression increases later, at 48 hours. This temporal discrepancy\nraises questions about whether SLFN11/12 induction is the cause of the tRNA reduction or a downstream\nconsequence. Clarification of this timeline, or additional data supporting a delayed effect of SLFN11/12,\nwould strengthen the authors' conclusion.\nANSWER #7:\nIn the revised manuscript, we have included a replicate experiment in which the Western blots signals were\nacquired under non-saturating conditions, allowing proper quantification (new Extended Data 9A and B).\nThese analyses show that SLFN11/12 expression begins to increase as early as 24 hours post-infection and\nthen gradually decline at later time points. This revised analysis aligns with the observed reduction in type II\ntRNAs at 32 hours post-infection, supporting the conclusion that SLFN11/12 upregulation contributes to the\nobserved decrease in tRNA abundance.\nPOINT #8\nAdd a graphical abstract or summary figure may help readers grasp the key findings.\nANSWER #8:\nThank you for the suggestion, we have now added a graphical summary in the revised manuscript, Extended\nData 11B.\nReviewer #3 (Remarks to the Author):\nCoronavirus genomes are enriched in suboptimal codons, which potentially reduce the rate of protein\nsynthesis. However, coronavirus proteins can be efficiently translated despite the codon usage conflict. To\ntackle this inconsistency, the authors uncovered that coronavirus infection changes tRNA-modifying enzyme\nexpression and tRNA epi-transcriptomics to modulate specific codon optimality. This manuscript shows the\ninteresting trends of tRNA modification changes and the corresponding enzyme expression. However, there\nare still many points that should be addressed before the publication.\nMajor comments:\nPOINT #1\nThe authors show attractive results in each figure, including tRNA epi-transcriptome changes. However, the\nrelationships between each finding and viral expression are missing. Authors should perform ribosome\nprofiling of virus-infected samples to investigate whether or not these RNA modification changes associate\nwith smooth ribosome elongation on virus mRNAs and host mRNAs.\nANSWER #1:\nWe fully agree that ribosome profiling could provide important insights into how tRNA modification changes\ninfluence translational elongation. However, under our infection conditions-when viral replication and\ntranslation are highly active, accurate ribosome profiling faces major technical limitations. As shown in\nprevious studies (See References 1–3 listed at the end of this response) the characteristic ribosome footprint\nperiodicity is lost in viral RNAs due to the accumulation of non-ribosomal, RNase-resistant RNA fragments.\nThese fragments likely arise from the association of multiple host and viral proteins with viral RNAs, masking\ngenuine ribosome footprints and obscuring codon-level analyses. Consequently, ribosome profiling of\ncoronaviruses (including SARS-CoV-2) is typically limited to early infection stages, when such artifacts are\nminimal (See References 1 and 2 listed at the end of this response). However, early-stage infections do not\ncapture the extensive host perturbations occurring during high replication conditions, the context in which\nwe observe infection-induced tRNA modification changes. To address the reviewer’s request and functionally\nlink tRNA modification changes with translational regulation, we performed two complementary analyses:\n1. Functional validation through enzyme perturbation:\nTo address this key issue, we focused on HCoV-OC43 infection on A549 cells because (i) the observed changes\nin tRNA modifications were conserved across both viruses analyzed, (ii) transfection efficiency in the Calu3\nand MRC5 cells lines (used for SARS CoV-2 and HCoV-OC43 infection, respectively) is poor and (iii) A549 cells\nsupport HCoV-OC43 replication (Extended Data 3D-F) and can be efficiently transfected.\nConsistent with a functional causal link between the observed tRNA epitranscriptome alterations and\nviral protein synthesis, we found that (i) siRNA-mediated knockdown of the tRNA modifying enzymes ELP3\n(mcm5U modification pathway) and QTRT1 (queuosine modification pathway), as well as (ii) overexpression\nof ADAT2 (A-to-I modification enzyme) and ALKBH1 (f5C pathway), each significantly reduced viral protein\nlevels (N-protein). In contrast, NSUN2 silencing had no detectable effect. The lack of an effect upon NSUN2\ndepletion, combined with the observed downregulation of ALKBH1 during infection and the strong inhibitory\neffect on viral N-protein expression upon its overexpression, suggests that the virus primarily modulates the\nNSUN3–ALKBH1 mitochondrial pathway, rather than the cytosolic NSUN2-dependent m⁵C modification. This\ninterpretation is further supported by the lack of detectable changes in global m⁵C tRNA modification levels\nduring infection (Figure 3A). We silenced ELP3 instead of KIAA1456 or ALKBH8 because it acts upstream in\nthe mcm⁵ modification pathway, directly influencing the activity of these enzymes, and because ELP3\nexpression itself was strongly upregulated during HCoV-OC43 infection (Figure 4A). Together, these findings\ndemonstrate that the observed alterations of the tRNA-modifying enzymes under infection conditions\nfunctionally contribute to coronavirus viral protein production. These results are now included in the new\nFigure 4 of the revised manuscript. The following text has been added to the manuscript at lines 273-291:\n“To establish a causal link between the altera2on of these enzymes and viral protein expression, we\nmanipulated the expression of selected tRNA modifying enzymes. These experiments were performed using\nHCoV-OC43 infec2on in A549 cells because the observed changes in tRNA modifica2ons were conserved across\nboth viruses analyzed, transfec2on efficiency in Calu3 and MRC5 cell lines (used for SARS-CoV-2 and HCoV-\nOC43 infec2on, respec2vely) is poor, and A549 cells support efficient HCoV-OC43 replica2on and transfec2on\n(Extended Data Figure 3D-F). Consistent with a func2onal link between tRNA epitranscriptome remodeling\nand viral protein produc2on, siRNA-mediated knockdown of ELP3 (mcm⁵U modifica2on pathway) and QTRT1\n(queuosine modifica2on pathway) significantly reduced viral protein levels (Figure 4A–B). ELP3 was selected\nfor silencing because it acts upstream of KIAA1456 and ALKBH8 in the mcm⁵U modifica2on pathway and is\nstrongly upregulated during HCoV-OC43 infec2on (Figure 4A). Similarly, overexpression of ADAT2 (A-to-I\nmodifica2on enzyme) or ALKBH1 (f⁵C pathway) also led to a pronounced decrease in viral protein expression\n(Figure 4D). In contrast, NSUN2 silencing had no detectable effect (Figure 4C). This lack of effect, together\nwith the observed downregula2on of ALKBH1 during infec2on (Figure 3H) and the strong inhibi2on of viral\nprotein produc2on upon its overexpression, suggests that viral infec2on primarily modulates the\nmitochondrial NSUN3–ALKBH1 pathway rather than the cytosolic NSUN2-dependent m⁵C modifica2on. This\ninterpreta2on is further supported by the absence of significant changes in global tRNA m⁵C levels analyzed\nby LC-MS/MS during infec2on (Figure 3A). Together, these findings demonstrate that altera2ons in mcm⁵U, Q\nand I tRNA-modifying enzymes during infec2on not only align with viral codon usage but also func2onally\ncontribute to coronavirus protein produc2on.”\nTogether, these results indicate that coronavirus infection remodels the host tRNA epitranscriptome\nmodulating codon optimality and enhancing viral protein translation. This functional relationship provides a\nmechanistic link between tRNA modification dynamics and viral gene expression, even in the absence of\ntechnically feasible ribosome profiling at peak replication stages.\n2. Codon optimality analysis:\nWe reasoned that the infection-induced tRNA modifications would cause a change in codon optimality that\nshould impact both viral and host mRNAs. Accordingly, we compared the codon composition of translation-\nactivated (TA) and translation-repressed (TR) host mRNAs under SARS-CoV-2 infection published by Kim D.\net al (See Reference 4 listed at the end of this response). We found that TA mRNAs were enriched in codons\npreferentially decoded by tRNAs bearing an I-unmodified (A-ending tRNAs), mcm⁵U and Q modifications,\nwhile TR mRNAs were enriched in codons preferentially decoded by I-modified tRNAs. These results are\nconsistent with SARS-CoV-2 codon preferences and indicate that infection drives a shift in codon optimality\nthrough remodeling of the tRNA epitranscriptome that favor viral protein synthesis. The corresponding\nanalyses are now presented in Extended Data 5 and the following text has been added to the manuscript at\nlines 292 – 299:\n“To further confirm the link between coronaviruses infection, tRNA modifications and viral protein expression,\nwe analyzed codon frequency in host transcripts translation-activated (TA) versus translation-repressed (TR)\nin ribosome profiling datasets from SARS-CoV-2–infected Calu3 cells. We reasoned that coronavirus-induced\nalterations in tRNA modifications would modify codon optimality, thereby influencing not only viral but also\nhost mRNA translation. Consistently, we found that, similar to coronavirus mRNAs, host TA mRNAs were\nenriched in codons preferentially decoded by tRNAs bearing an I-unmodified, mcm⁵U and Q modifications,\nwhereas TR mRNAs were enriched in codons preferentially decoded by I-modified tRNAs (Extended Data 5)”.\nReferences:\n1. Finkel, Y., Mizrahi, O., Nachshon, A. et al. The coding capacity of SARS-CoV-2. Nature 589, 125–130 (2021).\nhttps://doi.org/10.1038/s41586-020-2739-1;\n2. Finkel, Y., Gluck, A., Nachshon, A. et al. SARS-CoV-2 uses a multipronged strategy to impede host protein\nsynthesis. Nature 594, 240–245 (2021). https://doi.org/10.1038/s41586-021-03610-3;\n3. Jungfleisch, J., Böttcher, R., Talló-Parra, M. et al. CHIKV infection reprograms codon optimality to favor\nviral RNA translation by altering the tRNA epitranscriptome. Nat Commun 13, 4725 (2022).\nhttps://doi.org/10.1038/s41467-022-31835-x.\n4. Kim D. et al. “A high-resolution temporal atlas of the SARS-CoV-2 translatome and transcriptome”. Nat\nCommun 12, 5120 (2021) https://doi.org/10.1038/s41467-021-25361-5”\nPOINT #2\nTo investigate whether DNA damage response contributes to changes in tRNA modification under viral\ninfections, authors should check whether artificially induced DNA damage by chemicals (without viral\ninfection) shows reproducible epi-transcriptome results similar to viral infections, and confirm that DNA\ndamage inhibitors counteract the tRNA modifications by LC-MS/MS and tRNA-seq.\nANSWER #2:\nWe apologize for the lack of accuracy in the text. Indeed, several previous studies have extensively shown\nthat the tRNA epitranscriptome is tightly regulated under stress conditions, including the DNA damage\nresponse. the DNA damage response genes are enriched, as coronaviruses, in codons whose decoding is\nfavored by mcm5 modification in the counterpart tRNAs. We have now revised and updated the list of\nreferences (See References 1–7 listed at the end of this response).\nReferences:\n1. Mitchener M.M., Begley T.J,. Dedon P.C. Molecular Coping Mechanisms: Reprogramming tRNAs To\nRegulate Codon-Biased Translation of Stress Response Proteins. Acc Chem Res. 2024 Aug 20;57(16):2448.\nhttps://doi.org/10.1021/acs.accounts.3c00572;\n2. Chan C. et al. Reprogramming of tRNA modifications controls the oxidative stress response by codon-\nbiased translation of proteins. Nat Commun. 3, 937 (2012). https://doi.org/10.1038/ncomms1938;\n3. Begley U. et al. Trm9-catalyzed tRNA modifications link translation to the DNA damage response. Mol. Cell.\n4. Gu C., Begley T.J., Dedon P.C. tRNA modifications regulate translation during cellular stress. FEBS Lett. 2014\nNov 28;588(23):4287-96. https://doi.org/10.1016/j.febslet.2014.09.038;\n5. Huber S.M et al. Arsenite toxicity is regulated by queuine availability and oxidation-induced\nreprogramming of the human tRNA epitranscriptome. Proc Natl Acad Sci U S A. 2022 Sep\n20;119(38):e2123529119. https://doi.org/10.1073/pnas.2123529119;\n6. Rashad, S. et al. Translational response to mitochondrial stresses is orchestrated by tRNA modifications.\nbioRxiv 2024.02.14.580389 (2024) https://doi.org/10.1101/2024.02.14.580389.\n7. Patil A et al. Increased tRNA modification and gene-specific codon usage regulate cell cycle progression\nduring the DNA damage response. Cell Cycle. 2012 Oct 1;11(19):3656-65.\nhttps://doi.org/10.4161/cc.21919.\nPOINT #3\nTo uncover the relationship between upregulated expression of KIAA1456/ALKBH8, mcm5U modification,\ncodon optimality, and viral protein expression under viral infection, the authors should perform\nKIAA1456/ALKBH8 knockdown, check mcm5U modification, viral protein expression changes by Western\nblotting, and calculate ribosome occupancy across codons by ribosome profiling.\nANSWER #3:\nBecause of the ribosome profiling technical difficulties commented in Answer #1, we directly addressed the\nreviewer request by experimentally manipulating the relevant tRNA modification enzymes and testing the\neffect on viral protein levels, and testing the effect of viral infection on codon optimality. The results,\nalready\ndescribed in Answer #1, provide a functional link between the effect of SARS-CoV-2-induced tRNA\nepitranscriptome alterations and viral protein production.\nPOINT #4\nSimilarly, to check the contribution of SLFN11/12 to reducing specific tRNA abundance, the authors should\nperform SLFN11/12 knockdown, check tRNA abundance, and perform ribosome profiling to calculate\nribosome occupancy on target codons.\nANSWER #4:\nBoth SLFN11 and SLFN12. Are well established interferon-stimulated genes that cleave type II tRNAs (See\nReferences 1–5 listed at the end of this response). Consistently, in our data we show that SARS-CoV-2\ninfection, performed in an IFN-competent cell line, shows a selective reduction of type-II tRNAs that matches\nthese known SLFN-dependent cleavage patterns. By contrast, the HCoV-OC43 experiments were performed\nin an IFN-defective cell line, consistent with minimal SLFN induction and the absence of comparable tRNA\nlosses. Together, these literature-supported mechanisms and the distinct innate capacities of the two cell\nsystems provide the most plausible explanation for our observations. The manuscript now explicitly discusses\nthis interpretation at 342-350 includes references to the foundational SLFN11/12 literature.\n“Regarding the downregulated tRNAs, all belong to the type II tRNAs, which are substrates for Schlafen 11\n(SLFN11) and Schlafen 12 (SLFN12) endonucleases44. These enzymes are induced by the interferon (IFN)\nresponse as part of the innate antiviral defense or in response to DNA damage45–48. Consistent with the\ndownregulation of type II tRNAs in SARS-CoV-2 infection, but not in HCoV-OC43 infection, SARS-CoV-2\ninfection triggered an IFN response, as indicated by the expression of IFN-stimulated genes and the strong\nupregulation of SLFN11 and SLFN12 (Extended Data 9A-C). In contrast, HCoV-OC43 did not induce a proper\nIFN response and only led to a slight activation of IFNB1 at 72 hours post-infection, likely due to the DNA\ndamage response (Extended Data 9B-D). These differences are linked to the cell lines used for SARS-CoV-2\nand HCoV-OC43 experiments.”\nReferences:\n1. Kobayashi-Ishihara, M., Frazão Smutná, K., Alonso, F.E. et al. Schlafen 12 restricts HIV-1 latency reversal\nby a codon-usage dependent post-transcriptional block in CD4+ T cells. Commun Biol 6, 487 (2023).\nhttps://doi.org/10.1038/s42003-023-04841-y.\n2. Li, M., Kao, E., Malone, D. et al. DNA damage-induced cell death relies on SLFN11-dependent cleavage of\ndistinct type II tRNAs. Nat Struct Mol Biol 25, 1047–1058 (2018). https://doi.org/10.1038/s41594-018-\n0142-5\n3. Metzner, F.J., Wenzl, S.J., Kugler, M. et al. Mechanistic understanding of human SLFN11. Nat Commun 13,\n5464 (2022). https://doi.org/10.1038/s41467-022-33123-0\n4. Kim, E. T. & Weitzman, M. D. Schlafens Can Put Viruses to Sleep. Viruses 14, 442 (2022).\nhttps://doi.org/10.3390/v14020442\n5. Valdez F. et al. . Schlafen 11 Restricts Flavivirus Replication. J Virol 2019 93:10.1128/jvi.00104-19.\nhttps://doi.org/10.1128/jvi.00104-19\nPOINT #5\nTo confirm the relationship between oxidative stress, tRNA modification, and codon optimality, the authors\nshould chemically induce oxidative stress, examine its effects on enzyme expression regulation and changes\nin tRNA modifications such as queuosine and f5C, and check the codon optimality by ribosome profiling.\nANSWER #5:\nWe thank the reviewer for this comment. The link between oxidative stress, tRNA modification, and codon-\nbiased translation has been previously demonstrated as commented in Answer #2 (See References 1–6 listed\nat the end of this response). We have now included the complete set of references\nReferences:\n1. Mitchener M.M., Begley T.J,. Dedon P.C. Molecular Coping Mechanisms: Reprogramming tRNAs To\nRegulate Codon-Biased Translation of Stress Response Proteins. Acc Chem Res. 2024 Aug 20;57(16):2448.\nhttps://doi.org/10.1021/acs.accounts.3c00572;\n2. Chan C. et al. Reprogramming of tRNA modifications controls the oxidative stress response by codon-\nbiased translation of proteins. Nat Commun. 3, 937 (2012). https://doi.org/10.1038/ncomms1938;\n3. Begley U. et al. Trm9-catalyzed tRNA modifications link translation to the DNA damage response. Mol. Cell.\n4. Gu C., Begley T.J., Dedon P.C. tRNA modifications regulate translation during cellular stress. FEBS Lett. 2014\nNov 28;588(23):4287-96. https://doi.org/10.1016/j.febslet.2014.09.038;\n5. Huber S.M et al. Arsenite toxicity is regulated by queuine availability and oxidation-induced\nreprogramming of the human tRNA epitranscriptome. Proc Natl Acad Sci U S A. 2022 Sep\n20;119(38):e2123529119. https://doi.org/10.1073/pnas.2123529119;\n6. Rashad, S. et al. Translational response to mitochondrial stresses is orchestrated by tRNA modifications.\nbioRxiv 2024.02.14.580389 (2024) https://doi.org/10.1101/2024.02.14.580389.\nPOINT #6\nThe authors suggested that the protein level of the corresponding tRNA modification enzymes was changed\nupon viral infection and the associated tRNA modification per se. This reviewer guessed that the enzyme\nexpression comes first, but the mechanism was not studied. The authors should reveal the molecular\nmechanisms.\nANSWER #6:\nWe have now expanded our analyses to address temporal order and mode of regulation. Across MRC5 and\nA549 cells, RT–qPCR shows that tRNA-modifying enzyme mRNAs decrease during infection, whereas\nimmunoblotting demonstrates a time-dependent increase in the corresponding proteins (24–72 hpi),\nindicating post-transcriptional upregulation rather than transcriptional induction (Figure 3; Extended Data\n6). The following text has been added to the manuscript at lines 306-311:\n“Finally, to determine whether the expression of tRNA-modifying enzymes occurs at the transcriptional or\ntranslational level during infection, we analyzed their mRNA and protein abundance. qPCR analyses revealed\nthat the mRNA levels were decreased (Extended Data 6A-F), whereas western blot analyses showed a\nconcomitant increase in the protein levels (Figure 3G-J; Extended Data 6G). These results indicate that\nexpression of tRNA-modifying enzymes is primarily regulated at the translational level rather than\ntranscriptionally during infection”.\nThe rise in enzyme protein, precedes or coincides with the observed shifts in tRNA modification levels (Figure\n3), supporting that enzyme accumulation drives the modification changes. Pharmacological inhibition of the\nDNA-damage response (DDR) reduces viral protein production (Extended Data 2C), placing enzyme\nupregulation downstream of virus-activated stress/DDR signaling (lines 170 – 177). Functionally, siRNA\nknockdown of key enzymes reduces viral protein accumulation (Figure 4), establishing their contribution to\nefficient infection (lines 273-291). Collectively, these data support a model in which virus-induced stress/DDR\npathways promote translation of tRNA-modifying enzymes, reprogramming the tRNA-modification\nlandscape, consequently enhancing viral protein synthesis. We have now included a Graphical Summary to\nillustrate our model (Extended Data 11B).\nMinor comments:\nPOINT #7\nSome Western blot results are ambiguous; the authors should repeat the blotting experiments. Figure 2B:\npATM, ATM, and pATR bands are unclear, and GAPDH intensity appears to be saturated. Figure 6A:\nSLFN11/12 intensity appears to be saturated. The quantification of Western blots from triplicate (or more)\nwould be helpful. Additionally, this reviewer wondered why there are two GAPDH bands in Figure 6A/B.\nPlease explain the details.\nANSWER #7:\nWe have repeated the Western blot experiments to address the concerns. The revised figures now include\nhigher-quality blots obtained with exposures within the linear detection range, resulting in clearer pATM,\nATM, pATR and ATR signals (revised Figure 2B) as well as distinct SLFN11 and SLFN12 bands (revised new\nExtended Data 9 A and C).We also added quantification data from at least three independent biological\nreplicates, with densitometric values normalized to the corresponding loading controls (new Extended Data\nand we therefore displayed the respective loading controls for each. We agree that the original GAPDH\nsignals were oversaturated; consequently, we repeated the experiments using β-actin as an alternative\nloading control to prevent signal saturation.\nPOINT #8\nThe authors should check whether DDR inhibitors and/or oxidative stress inhibitors downregulate MAPK\nphosphorylation under viral infection.\nANSWER #8:\nPublished studies have shown that inhibitors of the DNA damage stress response pathway the use of and\nantioxidant impair coronaviruses infection (See References 1–4 listed at the end of this response).\nTo further validate this point and the link between DDR and oxidative stress, we showed that treatment of\nHCoV-OC43 infected cells with DDR inhibitors (ATM) reduced virus-induced MAPK phosphorylation (p-p38)\nand viral protein production. These data are included in the revised figures (Extended Data 2C) and the\nfollowing text has been added to the manuscript at lines 181 - 182:\n“Furthermore, inhibitors of the DNA damage stress response reduce both p-p38 levels and coronavirus\nprotein expression (Extended Data 2C).\nReferences:\n1. Liubing Du et al., Oxidative stress transforms 3CLpro into an insoluble and more active form to promote\nSARS-CoV-2 replication. Redox Biology, Volume 48, 2021, 102199, ISSN 2213-2317,\nhttps://doi.org/10.1016/j.redox.2021.102199.\n2. Olagnier D et al., SARS-CoV2-mediated suppression of NRF2-signaling reveals potent antiviral and anti-\ninflammatory activity of 4-octyl-itaconate and dimethyl fumarate. Nat Commun. 2020 Oct 2;11(1):4938.\nCorrection to: Nature Communications https://doi.org/10.1038/s41467-020-18764-3\n3. Ordonez, A.A., Bullen, C.K., Villabona-Rueda, A.F. et al. Sulforaphane exhibits antiviral activity against\npandemic SARS-CoV-2 and seasonal HCoV-OC43 coronaviruses in vitro and in mice. Commun Biol 5, 242\n(2022). https://doi.org/10.1038/s42003-022-03189-z\n4. Zhang, S., Wang, J., Wang, L. et al. SARS-CoV-2 virus NSP14 Impairs NRF2/HMOX1 activation by targeting\nSirtuin 1. Cell Mol Immunol 19, 872–882 (2022). https://doi.org/10.1038/s41423-022-00887-w\nPOINT #9\nHomo sapiens should be italicized in Figure 1 legends and Extended Data 1 legends.\nANSWER #9:\nCorrected as requested.\nPOINT #10\nThe text in the heatmap of Extended Data 6 is too small and needs adjustment for readability.\nANSWER #10:\nCorrected as requested (now Extended Data Figure 10).\nPOINT #11\nThe conclusion sentence in lines 172-174 was too strong from the data. The authors should tone down the\nnuance.\nANSWER #11:\nWe have now modified the text adding new data to support the statement, lines 178 -182.\n“These findings suggest that coronaviruses have adapted their codon usage to thrive in a tRNA environment\nin which stress responses are activated. Consistent with this, previous studies have shown that inhibitors of\nthe DNA damage stress response pathway and antioxidants impair coronaviruses infection. Furthermore,\ninhibitors of the DNA damage stress response reduce both p-p38 levels and coronavirus protein expression\n(Extended Data 2C)”.\nPOINT #12\nThe sentence in lines 214-217, the authors explained that f5C was found in mt-tRNAs. This was indeed true\nfor Q. This point should be highlighted.\nANSWER #12:\nWe thank the reviewer for the comment. We have now added the following sentence at lines 230 – 231.\n“Recent studies suggest that Q modification, as f⁵C, may also occur in mitochondrial tRNAs.\nPOINT #13\nThe authors should discuss manQ and galQ as well.\nANSWER #13:\nWe have now included the following text in the revised manuscript addressing the potential impact of manQ\nand galQ on viral codon translation at lines 136 – 139:\n“The Q modification and its derivatives, mannosyl-Q (manQ) and galactosyl-Q (galQ), favor the decoding of\nfour U-ending codons (Asparagine (Asn) AAU, Aspartic acid (Asp) GAU, Histidine (His) CAU, and Tyrosine (Tyr)\nUAU), with galQ found in human tRNATyr, and manQ found in human tRNAAsp (Figure 1D)”."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed the majority of my concerns, as well as those raised by the other reviewers, and have significantly improved the manuscript. This reviewer greatly appreciates the approach of knocking down relevant modification enzymes to demonstrate a cause-effect-relationship between tRNA modification patterns and viral protein synthesis. The observation that knockdown of ELP3 and QTRT1, as well as overexpression of ADAT2 and ALKBH1, reduces viral protein production is highly interesting. To fully address my concern the authors should make an effort to better visualize the results presented in Figure 4. Quantification of knockdown/overexpression efficiency and the corresponding effects on viral protein levels would greatly help the reader. It should also be noted that some knockdown effects appear quite subtle, raising doubts about their impact on the associated tRNA modifications. I suggest that, for at least one enzyme, the authors provide targeted tRNA modification analysis to demonstrate a reduction in the relevant modifications following knockdown.", "reviewer_2": "We have completed our review of the revised manuscript. The authors have been responsive to the comments and have adequately addressed the concerns raised in our previous review. The additional experiments/analyses and clarifications have strengthened the manuscript. We believe the revised version is now suitable for publication.", "reviewer_3": "This reviewer thanks the authors for their extensive revisions and thoughtful responses to this reviewer’s comments. The manuscript has been substantially improved. This reviewer appreciates the extensive efforts involved. The addition of enzyme perturbation experiments strengthens the work. However, this reviewer still has remaining concerns, primarily regarding the ribosome profiling analysis.\nThis reviewer appreciates the addition of enzyme knockdown/overexpression experiments and the codon frequency analysis shown in Extended Data 5. These are valuable data. However, this reviewer still respectfully and strongly requests doing ribosome profiling analysis.\nThe authors attribute their decision to omit ribosome profiling to technical limitations. However, this reviewer finds that this justification is premature. The authors cite a study (Finkel et al., 2021) as evidence that ribosome profiling at late infection stages is technically problematic due to loss of footprint periodicity on viral RNAs. However, another study (Kim et al., 2021), whose dataset the authors themselves use for the codon frequency analysis in Extended Data 5, appears to show clear 3-nucleotide periodicity at 36 hours post-infection. Thus, there is no technical hurdle for doing ribosome profiling in the author’s condition, according to the literature.\nThis reviewer recommended doing the new ribosome profiling experiments that matched the authors’ tRNA-Seq/mass spec data. Alternatively, the authors could reanalyze the Kim et al. dataset to calculate ribosome occupancy on each codon (viral and host mRNAs) (not the codon frequency analyzed in the Extended Data 5) and test the correspondence with tRNA modification changes (e.g., Q-modified, mcm5U-modified, and I-modified tRNAs).\nFor viral mRNAs, this reviewer understands the authors' concerns about non-ribosomal RNP complexes contributing to background signal, as noted in previous studies (Finkel et al., 2021; Jungfleisch et al., 2022). However, analytical strategies may address this issue. For instance, the authors could filter footprints by length, retaining only fragments in the 28-30 nucleotide range that match the canonical ribosome footprint size observed on host mRNAs. Alternatively, codon-specific metagene profiling, which averages ribosome occupancy around specific codons across many transcript instances, can reveal genuine translation signals even when background noise is present. Comparing such metagene profiles between mock-infected and infected samples (24-36 hpi) for codons affected by Q, mcm5U, and I modifications would directly test whether tRNA modification changes influence ribosome transit. This reviewer encourages the authors to explore whether such approaches could be applied.\nThe authors propose that tRNA-modifying enzymes are regulated post-transcriptionally during infection, based on decreased mRNA but increased protein levels (Figure 3G-J, Extended Data 6). This claim could be directly validated using the Kim et al. (2021) ribosome profiling dataset. Specifically, this reviewer asks the authors to analyze the translation efficiency (TE, calculated as ribosome-protected fragment reads / mRNA reads) of the key tRNA-modifying enzyme mRNAs —ELP3, QTRT1, ADAT2, ALKBH1, and NSUN2/3—across the infection time course to check the consistency with TE results in Extended Data 6.\nMinor points\nLine 136-139: citation of the paper is missing.", "reviewer_4": "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.", "reviewer_5": "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": "REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nPOINT #1\nThe authors have addressed the majority of my concerns, as well as those raised by the other reviewers, and\nhave significantly improved the manuscript. This reviewer greatly appreciates the approach of knocking down\nrelevant modification enzymes to demonstrate a cause-effect-relationship between tRNA modification patterns\nand viral protein synthesis. The observation that knockdown of ELP3 and QTRT1, as well as overexpression\nof ADAT2 and ALKBH1, reduces viral protein production is highly interesting. To fully address my concern\nthe authors should make an effort to better visualize the results presented in Figure 4. Quantification of\nknockdown/overexpression efficiency and the corresponding effects on viral protein levels would greatly help\nthe reader.\nANSWER #1\nWe redesigned Figure 4 to improve clarity and interpretability. The revised figure now includes quantification\nof knockdown and overexpression efficiency for each experiment, shown as bar graphs alongside\nrepresentative Western blots, as well as quantification of the corresponding effects on viral protein levels.\nPOINT #2\nIt should also be noted that some knockdown effects appear quite subtle, raising doubts about their impact on\nthe associated tRNA modifications. I suggest that, for at least one enzyme, the authors provide targeted tRNA\nmodification analysis to demonstrate a reduction in the relevant modifications following knockdown.\nSome knockdown effects appear subtle; for at least one enzyme, direct evidence of reduced tRNA modification\nis needed.\nANSWER #2\nWe now provide direct evidence that knockdown of the relevant enzyme reduces the corresponding tRNA\nmodification. LC–MS/MS analysis shows that ELP3 knockdown in HCoV-OC43 infected A549 cells\nsignificantly reduces the mcm⁵U modification.\nReviewer #2 (Remarks to the Author):\nWe have completed our review of the revised manuscript. The authors have been responsive to the comments\nand have adequately addressed the concerns raised in our previous review. The additional experiments/analyses\nand clarifications have strengthened the manuscript. We believe the revised version is now suitable for\npublication.\nReviewer #3 (Remarks to the Author):\nPOINT #1\nThis reviewer thanks the authors for their extensive revisions and thoughtful responses to this reviewer’s\ncomments. The manuscript has been substantially improved. This reviewer appreciates the extensive efforts\ninvolved. The addition of enzyme perturbation experiments strengthens the work. However, this reviewer still\nhas remaining concerns, primarily regarding the ribosome profiling analysis. This reviewer appreciates the\naddition of enzyme knockdown/overexpression experiments and the codon frequency analysis shown in\nExtended Data 5. These are valuable data. However, this reviewer still respectfully and strongly requests doing\nribosome profiling analysis. The authors attribute their decision to omit ribosome profiling to technical\nlimitations. However, this reviewer finds that this justification is premature. The authors cite a study (Finkel\net al., 2021) as evidence that ribosome profiling at late infection stages is technically problematic due to loss\nof footprint periodicity on viral RNAs. However, another study (Kim et al., 2021), whose dataset the authors\nthemselves use for the codon frequency analysis in Extended Data 5, appears to show clear 3-nucleotide\nperiodicity at 36 hours post-infection. Thus, there is no technical hurdle for doing ribosome profiling in the\nauthor’s condition, according to the literature. This reviewer recommended doing the new ribosome profiling\nexperiments that matched the authors’ tRNA-Seq/mass spec data. Alternatively, the authors could reanalyze\nthe Kim et al. dataset to calculate ribosome occupancy on each codon (viral and host mRNAs) (not the codon\nfrequency analyzed in the Extended Data 5) and test the correspondence with tRNA modification changes\n(e.g., Q-modified, mcm5U-modified, and I-modified tRNAs). For viral mRNAs, this reviewer understands the\nauthors' concerns about non-ribosomal RNP complexes contributing to background signal, as noted in previous\nstudies (Finkel et al., 2021; Jungfleisch et al., 2022). However, analytical strategies may address this issue. For\ninstance, the authors could filter footprints by length, retaining only fragments in the 28-30 nucleotide range\nthat match the canonical ribosome footprint size observed on host mRNAs. Alternatively, codon-specific\nmetagene profiling, which averages ribosome occupancy around specific codons across many transcript\ninstances, can reveal genuine translation signals even when background noise is present. Comparing such\nmetagene profiles between mock-infected and infected samples (24-36 hpi) for codons affected by Q, mcm5U,\nand I modifications would directly test whether tRNA modification changes influence ribosome transit. This\nreviewer encourages the authors to explore whether such approaches could be applied. The authors propose\nthat tRNA-modifying enzymes are regulated post-transcriptionally during infection, based on decreased\nmRNA but increased protein levels (Figure 3G-J, Extended Data 6). This claim could be directly validated\nusing the Kim et al. (2021) ribosome profiling dataset. Specifically, this reviewer asks the authors to analyze\nthe translation efficiency (TE, calculated as ribosome-protected fragment reads / mRNA reads) of the key\ntRNA-modifying enzyme mRNAs—ELP3, QTRT1, ADAT2, ALKBH1, and NSUN2/3—across the infection\ntime course to check the consistency with TE results in Extended Data 6.\nANSWER #1\nWe performed an in-depth reanalysis of the Kim et al. (2021) ribosome profiling dataset:\n1. Host mRNAs enriched in codons corresponding to infection-induced tRNA modifications display\nreduced ribosome occupancy, consistent with accelerated translation (Extended Data Figure 5D–F).\nThis sentence as now been added to the manuscript at lines 300 -302:\n“Moreover, ribosome occupancy analyses confirmed faster translation elongation at infection-induced\ntRNA-modification–dependent codons in TA transcripts relative to TR ones (Extended Data 5 D-F)”.\n2. Ribosome occupancy of tRNA-modifying enzymes could not be reliably assessed due to insufficient\nread coverage in the available dataset. However, the discordance between protein and mRNA levels\n(Extended Data 6), together with the clear reduction in viral protein levels observed upon knockdown\nof these enzymes (Figure 4), supports a translation-level activation during infection. In the revised\ntext, we maintain this interpretation but adopt more cautious wording, using “suggest” rather than\n”indicate” (line 312).\n3. Viral ribosome occupancy cannot be directly compared with mock conditions because viral sequences\nare absent from mock samples, precluding differential occupancy analyses.\nPOINT #2\nLine 136-139: citation of the paper is missing.\nANSWER #2\nThe citation has been added to the manuscript."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have adequately addressed my issues and have improved the manuscript. I do not have additional comments.", "reviewer_3": "The authors answered the remaining concerns of this reviewer. The one note could be for the new sentence of “Moreover, ribosome occupancy analyses confirmed faster translation elongation at infection-induced tRNA-modification–dependent codons in TA transcripts relative to TR ones (Extended Data 5 D-F)” in lines 300 -302. If this reviewer’s understanding is correct, the analysis in Extended Data 5D-F did not focus on the specific subgroup of mRNAs, but rather global changes in all the mRNAs in the dataset. Thus, the phrase regarding TA transcripts and TR transcripts in the corresponding sentence should be irrelevant. The authors should carefully revisit their own analysis and description of the data.\nAlso, the sentence summarized that all the corresponding codons provided faster elongation. However, this may not apply to some of the codons. The authors should rewrite this part more concretely, not exaggerating the results.", "reviewer_4": "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": "Point by point response.\nReviewer comment:\nThe authors answered the remaining concerns of this reviewer. The one note could be for the new sentence of\n“Moreover, ribosome occupancy analyses confirmed faster translation elongation at infection-induced tRNA-\nmodification–dependent codons in TA transcripts relative to TR ones (Extended Data 5 D-F)” in lines 300 -\n302. If this reviewer’s understanding is correct, the analysis in Extended Data 5D-F did not focus on the\nspecific subgroup of mRNAs, but rather global changes in all the mRNAs in the dataset. Thus, the phrase\nregarding TA transcripts and TR transcripts in the corresponding sentence should be irrelevant. The authors\nshould carefully revisit their own analysis and description of the data.\nAlso, the sentence summarized that all the corresponding codons provided faster elongation. However, this\nmay not apply to some of the codons. The authors should rewrite this part more concretely, not exaggerating\nthe results.\nAnswer:\nWe have adjusted the sentence at lines 301 – 302 that now states:\n“Moreover, ribosome occupancy analyses revealed reduced occupancy at these codons in in line with enhanced\ntranslation elongation during infection (Supplementary Figure 5 D-F)”."}]

Decision letter