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Current understanding of mitochondrial stress responses is largely defined by well-characterized transcriptional pathways, yet the specific role of tRNA modifications in fine-tuning selective translational reprogramming remains a significant gap in our knowledge. This manuscript by Rashad et al. investigates how the tRNA epitranscriptome contributes to cellular adaptation during mitochondrial stress. Using a multi-omics approach (RNA-seq, Ribo-seq, LC-MS/MS for tRNA modifications), the authors show that mitochondrial stress induces dynamic and stressor-specific alterations in several key tRNA modifications (notably Q, f5C, hm5C, and mcm5U), which in turn influence codon decoding and reprogram translation. This is an interesting study that provides a new perspective on the decoupling of transcription and translation during cellular stress. However, the manuscript would be significantly strengthened by exploring plausible mechanisms regulating the activity of the modification enzymes, and by incorporating a discussion on inherent codon optimality biases to provide a more nuanced and robust interpretation of the findings.

Major Comments

1. Mechanistic basis of dynamic tRNA modification changes is insufficiently explored

The authors identify robust stress-induced changes in multiple tRNA modifications while observing no substantial changes in expression of their corresponding modifying enzymes (e.g., ALKBH1, QTRT1/2). This strongly suggests regulation at the level of enzyme activity rather than expression. The manuscript currently acknowledges this but does not explore plausible mechanisms. Additional discussion on potential regulatory modes—such as allosteric control by stress-altered metabolites, changes in cofactor availability, or post-translational modifications of these enzymes under ISR/RSR pathways—would significantly strengthen the mechanistic interpretation.

2. Codon preference under stress may reflect codon optimality rather than tRNA modification dominance

Across all tested stress conditions, translationally upregulated genes consistently show enrichment in G/C-ending codons, whereas downregulated genes are enriched for AT-ending codons. The authors attribute this pattern primarily to stress-induced tRNA modification changes. However, in mammals, G/C-ending codons are inherently more optimal under basal conditions. Therefore, an alternative explanation could be considered: under stress, translational resources become limiting, and naturally “stronger” (rich in G/C-ending codons) transcripts may outcompete “weaker” (rich in A/T-ending codons) transcripts. Unless this alternative is ruled out, the current interpretation overstates the dominant role of tRNA modifications. The authors should consider rephrasing their claims and discussing how tRNA modification dynamics may fine-tune or amplify an already existing optimality bias rather than dictate the entire translational program.

3. Divergent phenotypes between QTRT1 and QTRT2 KO cells require deeper interpretation

Although QTRT1 and QTRT2 form subunits of the same TGT complex, their knockout phenotypes differ markedly. The low correlation between the two KOs in epitranscriptomic (R = 0.25) and metabolic (R = 0.51) profiles, together with distinct cellular outcomes (e.g., strong translational repression in QTRT1 KO but not QTRT2 KO; increased P-bodies in QTRT2 KO), is unexpected and intriguing. The authors could discuss possible mechanisms, including subunit-specific noncanonical roles, differential stability or assembly dynamics of the complex, or distinct interactions with other pathways. This divergence is potentially important because it implies that the observed translational phenotypes may not be solely attributable to the loss of tRNA-Q, but potentially confounded by subunit-specific functions. Therefore, it deserves explicit attention.

4. Overly strong statements regarding the dominance of tRNA modification-driven translational control

For example, the phrasing in p.4 “the translational program was governed by a set of tRNA modification changes that alter…” is overly absolute. Translational control during stress involves multiple regulatory layers, including changes in aaRS expression, altered tRNA transcription or abundance, global translation initiation pathways, and ribosome allocation. The authors’ central model—that mitochondrial-stress-induced translational reprogramming is primarily driven by changes in tRNA modifications—would benefit from more nuanced framing that also considers these additional regulatory contributions. I suggest revising the text to acknowledge these additional regulatory contributions and positioning tRNA modifications as a critical tuning layer rather than the sole governor of the response.

Minor Comments

p.6: The statement “It was apparent that at different levels of analysis, rotenone and antimycin stress responses correlated much better than with arsenite stress response” is vague. The authors could improve clarity by specifying what “levels of analysis” are being referenced (e.g., transcriptomic patterns, ribosome occupancy).

Competing interests

The authors declare that they have no competing interests.

Use of Artificial Intelligence (AI)

The authors declare that they did not use generative AI to come up with new ideas for their review.