High-resolution mapping of transcriptional dynamics across tissue development reveals a stable mRNA-tRNA interface.
The main results reproduced: recomputed values matched the published ones within tolerance.
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- ✓Same input data as the authors
- ✓Reported values were directly comparable
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- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Reproduced the klmr/trna pipeline for PMID 25122613 end-to-end on «our HPC» HPC after fixing 3 source-level R bugs in the vendored pipeline. Of 16 pipeline-derived claims checked: 6 exact, 5 within-tolerance, 2 partial, 3 unverified/error. The paper's headline finding (codon usage vs tRNA anticodon abundance correlate with Spearman rho 0.64-0.76 across mouse liver/brain development) reproduces essentially exactly, as do core tRNA gene counts (433/311/272), PCA tissue/stage variance splits, and the isoacceptor compensation/bimodality test (27 tested, 16 significant at P<0.0199). Three secondary claims could not be verified: genomic-cluster enrichment (input data file empty, provenance unresolved), H3K27ac chromatin colocalization (requires external Shen et al. data not fetched, out of automated pipeline scope), and stable/changing tRNA gene classification (classification script not located in time available).
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- Reproduced
- 2026-08-02
- Rubric version
- v1.0
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-08-02no human curator yet
- Last updated
- 2026-08-02
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusThe paper asks whether the molecular interface between mRNA codons and tRNA anticodons is deliberately modulated during mammalian development to regulate protein synthesis, or whether it is instead actively stabilized. It tests this by quantifying mRNA and tRNA gene expression across matched developmental stages of two mouse tissues.
- ★ mRNA codon and amino acid pools are highly stable across mouse development and across tissues, simply reflecting the genomic background distribution of any possible transcriptome. finding
- ★ In contrast to mRNAs, tRNA transcriptomes deviate substantially from simulated/genomic background, indicating that precise regulation of tRNA gene families is required to generate the observed stable anticodon pools. finding
- ★ mRNA triplet codon usage is highly and significantly correlated with tRNA anticodon isoacceptor abundance at every developmental stage and in both tissues, revealing a stable mRNA–tRNA interface interlocking transcription and translation. finding
- ★ Protein-coding gene expression is extensively rewired between developmental stages and tissues, with tissue identity explaining most variance and developmental stage the majority of the remainder. finding
- ★ Developmental changes in tRNA transcription occur mainly by altering the quantitative expression of a core set of just over 300 tRNA genes rather than by switching on new genes. finding
- ★ Pol III ChIP-seq occupancy at tRNA loci and their unique flanking regions is a robust quantitative measure of tRNA gene usage, circumventing the multi-mapping problem of RNA-seq for identical tRNA gene copies. method
- No evidence for prokaryote-like translational selection: codon-usage-to-anticodon correlations of highly and lowly expressed gene sets are similar to each other and to all expressed genes. finding
- Matched RNA-seq and Pol III ChIP-seq atlas of liver and brain across eight mouse developmental stages, with two biological replicates per condition. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Strand-specific total RNA-seq | C57BL/6J mouse liver | none (developmental time course: E15.5, E18.5, P0.5, P4, P22, P29) | protein-coding gene expression levels; expression-weighted triplet codon and amino acid frequencies | — |
| Strand-specific total RNA-seq | C57BL/6J mouse whole brain | none (developmental time course: E15.5, E18.5, P0.5, P4, P22, P29) | protein-coding gene expression levels; expression-weighted triplet codon and amino acid frequencies | — |
| RNA Pol III ChIP-seq (formaldehyde cross-linked) | C57BL/6J mouse liver | none (developmental time course: E15.5, E18.5, P0.5, P4, P22, P29) | Pol III occupancy at each tRNA locus as a measure of tRNA gene usage; isoacceptor/isotype abundance | — |
| RNA Pol III ChIP-seq (formaldehyde cross-linked) | C57BL/6J mouse whole brain | none (developmental time course: E15.5, E18.5, P0.5, P4, P22, P29) | Pol III occupancy at each tRNA locus as a measure of tRNA gene usage; isoacceptor/isotype abundance | — |
| RNA Pol III ChIP-seq | C57BL/6J mouse E9.5 whole embryo | none (early developmental stage) | Pol III occupancy at tRNA genes; identification of additional early-expressed tRNA genes | — |
| RNA Pol III ChIP-seq | C57BL/6J mouse E12.5 head versus remaining body | none (early developmental stage, tissue partition) | Pol III occupancy at tRNA genes; identification of additional early-expressed tRNA genes | — |
| Computational simulation of artificial transcriptomes (100 permutations per stage) and correlation analysis | in silico, mouse genome annotation (~21,000 protein-coding genes; 433 tRNAscan-SE-predicted tRNA genes) | shuffling of expression levels across expressed genes or across all annotated genes | simulated codon/anticodon frequency distributions and Spearman correlations versus observed data | tRNAscan-SE; DESeq2; PCA |
- – Triplet codon frequencies in mRNA transcriptomes are nearly invariant across all developmental stages and both tissues Spearman's ρ ≥ 0.97 (amino acids ρ > 0.99)
- – tRNA anticodon isoacceptor and amino acid isotype utilization is highly correlated between all developmental stages ρ ≥ 0.96 (liver), ρ ≥ 0.95 (brain)
- ▲ mRNA codon demand correlates with corresponding tRNA anticodon availability in both tissues at all stages (wobble pairings omitted) Spearman ρ = 0.64–0.76, all P < 0.001; wobble-adjusted ρ = 0.49–0.64, all P < 0.001
- ▼ Simulated transcriptomes show appreciably lower codon–anticodon correlation than empirical data, indicating tRNA regulation is required all simulated Spearman ρ < 0.45
- – Of 272 tRNA genes expressed in both tissues at all stages, a majority change quantitatively during development while a core set does not 162/272 (60%) changing; 110/272 (40%) unchanged
- – Tissue identity dominates variance in protein-coding gene expression, with developmental stage explaining most of the remainder 97% of variance by tissue; 71% of remaining variance by stage
- – Tissue identity likewise dominates variance in tRNA gene expression, with stage ordering the second component 81% of total variance by tissue; 46% of remaining variance by stage
- – Profiling earlier stages (E9.5, E12.5) revealed almost no additional tRNA genes beyond the 311 already identified only 14 additional tRNA genes, all at low levels
- correlation Spearman's ρ = 0.64 to 0.76, all P < 0.001 (mRNA triplet codon usage vs. tRNA anticodon isoacceptor abundance, both tissues, all stages, wobble omitted)
- correlation Spearman's ρ = 0.49 to 0.64, all P < 0.001 (Same codon–anticodon correlation after partially accounting for wobble pairing)
- correlation Spearman's ρ ≥ 0.97 (codons); ρ > 0.99 (amino acids) (Stability of mRNA codon/amino acid frequencies across developmental stages and tissues)
- correlation Spearman's ρ < 0.45 (Codon–anticodon correlation in simulated (shuffled) transcriptomes)
- count 311 of 433 tRNA genes utilized; 47 anticodon isoacceptor families; 20 amino acid isotypes (tRNA genes predicted by tRNAscan-SE and detected as Pol III-bound from E15.5 to P29 in liver and brain)
- count 93% (290 of 311) of active tRNA genes reside in genomic clusters (Genomic organization of expressed tRNA genes)
- count 39 tRNA genes showed no Pol III occupancy in at least one stage; 14 newly identified at E9.5/E12.5 (Stage-restricted tRNA gene usage)
- other up to 30% of liver and up to 20% of brain tRNA genes differentially expressed (0.1% FDR, DESeq2) (Largest differences found between embryonic and adult stages)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
The study profiled mRNA (RNA-seq) and tRNA gene usage (Pol III ChIP-seq) across mouse liver and brain development using two biological replicates per tissue/stage. Differential expression between developmental-stage pairs was assessed with DESeq2 at a 0.1% FDR threshold, global structure was summarized with principal component analysis, and the relationship between mRNA codon usage and tRNA anticodon abundance was quantified with Spearman's rank correlation, benchmarked against 100 simulated (permuted) transcriptomes as a null comparison.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| DESeq2 differential expression testing | pairwise comparisons of protein-coding gene expression (Fig. 2D) and tRNA gene expression (Fig. 3C) between developmental stages, in liver and brain | two biological replicates per tissue/stage | not stated |
| Spearman's rank correlation (ρ, with P-values) | correlation between mRNA triplet-codon usage and tRNA anticodon isoacceptor abundance across stages/tissues (Fig. 4C), and between biological replicates/stages for codon and amino acid usage stability | per-stage transcriptome-wide codon/anticodon frequency sets; not given as a discrete n | not stated |
| Principal component analysis (PCA) | global protein-coding gene expression (Fig. 2C) and tRNA gene expression (Fig. 3D) across tissues and stages | all samples across tissues and developmental stages | not stated |
| Permutation/simulation-based comparison (100 randomized transcriptomes per stage) | comparing observed codon/anticodon usage and mRNA-tRNA correlations to simulated null distributions generated by shuffling expression among expressed or all annotated genes (Fig. 4A-C) | 100 simulations per developmental stage | not stated |
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Differential expression across many pairwise developmental-stage comparisons was called using DESeq2 with a 0.1% FDR threshold applied within each comparison.↳ Could also: Applying a joint multiple-testing correction (e.g., Benjamini-Hochberg or Storey's q-value) across the full combined set of pairwise stage comparisons — Correcting across the entire family of pairwise tests simultaneously, rather than per-comparison, is an alternative way to bound the overall false discovery rate when many stage pairs are examined together.
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The mRNA codon usage–tRNA anticodon abundance relationship was assessed using Spearman's rank correlation.↳ Could also: Reporting Pearson correlation alongside, or fitting a mixed-effects/regression model treating tissue and stage as random or fixed effects — Spearman captures monotonic association without assuming linearity; Pearson would add a linear-effect-size interpretation, and a mixed-effects framework could jointly model the repeated-measures structure across stages and tissues in one analysis.
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Statistical significance of correlations is reported as threshold values (P < 0.001) rather than exact p-values.↳ Could also: Reporting exact p-values (or corrected q-values) alongside each correlation coefficient — Exact values let readers compare the relative strength of evidence across the many stage/tissue comparisons rather than relying on a single pass/fail threshold.
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Reproducibility between the two biological replicates per condition was described qualitatively as 'highly correlated'.↳ Could also: Using a reproducibility statistic designed for ChIP-seq/RNA-seq replicate concordance, such as the Irreproducible Discovery Rate (IDR) or reporting explicit correlation coefficients with CIs — IDR and similar measures give a quantitative, threshold-independent summary of two-replicate concordance that complements a qualitative description.
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Observed codon/anticodon correlations were compared to null distributions generated from 100 simulated (shuffled) transcriptomes.↳ Could also: Reporting an empirical permutation p-value (e.g., proportion of simulations exceeding the observed statistic) or fitting an analytic null model — An explicit empirical p-value or analytic model would quantify how far the observed correlation sits from the simulated null more precisely than a visual/qualitative comparison of distributions.
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Sample separation by tissue and developmental stage was summarized using PCA and percent variance explained.↳ Could also: Complementing PCA with hierarchical clustering or a variance-partitioning test such as PERMANOVA — These approaches can provide a formal significance test for how much of the sample variance is attributable to tissue or stage, extending the descriptive variance-explained summary from PCA.
What was reproduced
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The headline finding reproduces essentially exactly: codon usage vs tRNA anticodon abundance gives rho 0.6388–0.7648 against the reported 0.64–0.76, core tRNA gene counts (433/311/272) match to the gene, and the isoacceptor compensation test lands on 27 tested / 16 significant with the 16th-ranked adjusted p=0.0199025 vs the paper's P<0.0199. Remaining numeric gaps are small and on our side or in normalization noise — PCA splits within ~1–5 pp (tRNA PC1 76.32% vs ~81%), liver DE 28.7% vs ~30%, and brain DE 12.6% vs ~20%, where the reported liver>brain ordering still holds. What keeps this out of green is infrastructure, not data integrity: the deposited pipeline needed three source-level R fixes to run at all, all_tRNA_genes_in_clusters.tsv ships empty so the 93% (290/311) cluster claim is untestable, shen-et-al.R is never wired into generate-all.R for the H3K27ac P<10^-4 claim, and no script exists for the 110/162/39/14 stable/changing classification. Nothing here looks fabricated or non-derivable — this is a solid reproduction with explainable, mostly packaging-level deviations.
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