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The exonuclease Xrn1 activates transcription and translation of mRNAs encoding membrane proteins.

Nat Commun · 2019
L1 85/100 PQI 95
Why this verdict

The main results reproduced: recomputed values matched the published ones within tolerance.

Reproduced on the brainbox compute brainarbeit.com
Scoring basis — itemised

Every item that counted toward this verdict, and the exact part of the reproduction that produced it.

Supporting (toward a concern)
Content-critical question only partially held
+2 pts
From: Q8 · Severity of the miss (overall human judgment) 🟡
Minor / cosmetic deviation
+1 pts
From: Q3 · Location of the main deviation 🟡
Concordant (toward reproduced)
Code + data deposited & functional
-2 pts
From: Data & code availability Available & functional
Total score +1
✓ What held up
  • Same input data as the authors
  • Reported values were directly comparable
  • No authors-side cause for any deviation
  • Reported values are derivable from the shared data
  • Any deviation was negligible
  • The central claim held under reproduction
What did not (or only partly)
  • 🟡A deviation arose in the data or preprocessing
  • 🟡Overall, the reproduction showed a material discrepancy
How its reproducibility compares
85/100
Reproducibility score
0.6 SD above mean
vs. all fields · 1173 studies
🎯 Scores higher than 67% of all assessed papers rank 348 of 1173 scored

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

Described well enough to reproduce. The harvested code link github.com/abelew/prfdb is a MIS-HARVEST (unrelated 2009 Perl frameshift tool; the paper ships no code). Per brief rule 2/P16 we reproduced the headline computational result by applying the DOCUMENTED third-party pipeline (Riborex v1.2.3, engine DESeq2, FDR<0.05) to the GEO-shipped processed count tables for GSE109734 (RPF + mRNA footprint/ counts). The paper's auxin-degron acute-depletion contrast (t0 vs t30) yields 429 translationally-activated and 585 repressed genes vs the reported 445 / 597 -- within ~2-4%, same direction and magnitude => within-tol, NO fabrication signal. The alternative steady-state WT-vs-D208A(catalytic-dead) contrast gives 1030/1059 (not the paper's basis). NOT attempted (out of 80/20 primary scope): R2 membrane-protein GO enrichment (2.09x, needs gProfileR+REViGO) and R3 5'UTR-length comparison (needs UTR annotation); both are secondary. Verdict is automated/provisional; a human must confirm. Key infra hurdle solved: Bioconductor data CDN (mghp.osn.xsede.org) is unreachable from «our HPC», breaking genomeinfodbdata's conda post-link -- fixed by repointing dataURLs.json to the TU-Dortmund Bioconductor mirror + CONDA_SAFETY_CHECKS=disabled.

These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.

Assessment versions

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  1. v1 current initial assessment Score 85
    assessed: 2026-06-16 ⛓ d1ebec816788
✎ I am an author of this paper

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Provenance — full disclosure

When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.

Reproduced
2026-06-16
Rubric version
v1.0
Assessed by
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16
no human curator yet
Last updated
2026-08-05

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: opus
Founding hypothesis

The authors test whether the 5'–3' exonuclease Xrn1, known to couple nuclear transcription to cytoplasmic mRNA decay, has an additional regulatory role in mRNA translation—specifically whether it promotes translation of a defined group of transcripts encoding membrane proteins.

Core claims
  • Xrn1 promotes translation of a specific group of mRNAs encoding membrane/secretome proteins, acting at translation initiation. finding
  • Xrn1-dependence for translation is mediated by a physical, RNase-resistant interaction of the Xrn1 C-terminal domain with the translation initiation factor eIF4G. mechanism
  • For membrane-protein mRNAs, Xrn1 coordinately stimulates transcription, translation, decay, and ER localization, linking the three major stages of gene expression. finding
  • Xrn1's translational role is specific and not simply due to its 5'-3' exonuclease activity, since cytoplasmic Rat1∆NLS rescues decay/growth but not translation. finding
  • Xrn1-dependent translation is linked to mRNAs with long, highly structured 5'UTRs that provide poor contexts for translation initiation. mechanism
  • The BMV RNA2/yeast system and ribosome-profiling with an auxin-inducible degron (AID) were used to define Xrn1-dependent translation. method
  • A Rat1∆NLS-XC chimera (Rat1 N-terminal domain fused to Xrn1 C-terminal tail) gains eIF4G interaction and rescues RNA2 translation, a gain-of-function demonstration. method
Experimental setups
Assay System Perturbation Readout Platform
Western blot + Northern blot (steady-state protein 2a and viral RNA2) S. cerevisiae WT and xrn1∆ strains expressing BMV RNA2 from GAL1 promoter XRN1 deletion (KO); UTR/CDS swaps (GAL1 5'UTR, ADH1 3'UTR, GFP CDS) 2a protein and RNA2 levels; translatability (Δprotein/ΔRNA)
Luciferase assay (2a-Rluc) with cycloheximide chase yeast WT and xrn1∆ XRN1 deletion; cycloheximide translation block Rluc activity / protein turnover and translatability
Polysome profiling (sucrose gradient 10–50%) + Northern blot yeast WT and xrn1∆ expressing RNA2 XRN1 deletion; EDTA treatment control distribution of RNA2 across ribosomal fractions; rRNA UV profile at 260 nm
Polysome profiling + Western blot (Xrn1 cofractionation) yeast WT cells none Xrn1, S8 (40S), L1 (60S) protein distribution across fractions
Auxin-inducible degron depletion + RT-qPCR + luciferase yeast with AID-tagged genomic XRN1 and plasmid RNA2-Rluc (GAL1) auxin-induced Xrn1-AID degradation; galactose induction Xrn1-AID protein level, RNA2-Rluc RNA, 2a-Rluc protein over time
mRNA stability / decay time-course (transcription shut-off) + growth curves xrn1∆ cells expressing WT Xrn1, Rat1∆NLS, or empty plasmid glucose-induced GAL1 shut-off; Rat1∆NLS or chimera expression RNA2 levels over time; cell growth
Co-immunoprecipitation + Western blot (eIF4F interaction) yeast strains with genomic GFP-tagged eIF4G/eIF4A/eIF4E + plasmid FLAG-Xrn1 or FLAG-Rat1∆NLS / Rat1∆NLS-XC ±RNase A treatment; GFP-trap pulldown co-precipitation of Xrn1/Rat1 with eIF4G/eIF4A/eIF4E GFP-trap beads; anti-GFP and anti-FLAG antibodies
Ribosome profiling (RPF deep-sequencing) + parallel RNA-seq yeast Xrn1-AID strain auxin treatment (30 min) for Xrn1 knock-down genome-wide translational efficiency (mRNA vs ribosome occupancy log2FC), GO enrichment
Key results
  • Xrn1 depletion (xrn1∆) increased steady-state RNA2 level but substantially decreased 2a protein; translatability in xrn1∆ was only 0.4% of WT. 0.4% of WT (0.5% considering only capped RNA2)
  • Most RNA2 molecules are capped in the cells analyzed. 77%
  • The 5'UTR is the most Xrn1-responsive region for translatability; 3'UTR swap had no effect and CDS swap had modest effect.
  • Xrn1 deletion shifted RNA2 from polysomes toward monosomal, 60S and 40S fractions, indicating an initiation defect; Xrn1 co-sediments with free 40S subunits.
  • Xrn1, but not Rat1∆NLS, co-immunoprecipitated with eIF4G in an RNase-resistant manner; neither interacted with eIF4A; both showed RNase-sensitive eIF4E interaction.
  • Rat1∆NLS-XC chimera gained eIF4G interaction and increased viral 2a expression relative to Rat1∆NLS while RNA2 levels stayed similar. twofold (2x) increase in 2a
  • Ribosome profiling identified a set of genes translationally activated (445) or repressed (597) by Xrn1; activated/buffered mRNAs enriched for glycosylation, membrane and ER GO terms. 445 activated, 597 repressed
  • Rat1∆NLS fully rescued RNA2 decay and growth in xrn1∆ but only marginally recovered 2a protein, showing translation requires an Xrn1-specific function.
Key statistics
  • other 0.4% (translatability of RNA2 in xrn1∆ relative to WT (100%))
  • count 77% (fraction of RNA2 molecules that are capped)
  • fold_change twofold (increase in viral 2a expression with Rat1∆NLS-XC vs Rat1∆NLS)
  • count 445 activated / 597 repressed (genes translationally activated or repressed by Xrn1 (ribosome profiling))
  • pvalue FDR < 0.05 (Riborex threshold for significant translational efficiency changes)
  • other (log2 fold change) < 0.433 (~±35%) (threshold for considering mRNA levels stable/buffered)
  • count n = 3 (biological replicates/colonies for western/northern quantifications)
  • count n = 2 (replicates per condition for ribosome-profiling RPF/RNAseq libraries)

Statistical methods review

Model: opus

A 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 combines transcriptome-wide ribosome-profiling (RPF + parallel RNAseq) with targeted molecular assays (western/northern blots, RT-qPCR, luciferase, polysome profiling, co-immunoprecipitation). Genome-wide translational-efficiency changes were called with the Riborex R-package (FDR < 0.05) using DESeq2-derived log2 fold changes, with replicate quality assessed by principal-component analysis and functional groups characterized by GO/GSEA enrichment. Low-throughput experiments were typically summarized as mean ± SEM from n = 3 independent colonies/biological replicates, with individual data points overlaid.

Replicationbiological Sample sizeReported per experiment as n = 3 independent colonies / biological replicates for low-throughput assays and n = 2 biological replicates per condition for ribosome profiling; no formal power/sample-size calculation described GroupsWT vs xrn1∆ / Xrn1-AID-depleted (Xrn1-KD); also Rat1∆NLS and Rat1∆NLS-XC rescue conditions Pairingunclear Randomization/blindingnot stated DispersionSEM Exact p-valuesno Effect sizesyes Confidence intervalsno Multiplicity correctionFDR threshold (FDR < 0.05) applied within Riborex/DESeq2 output; specific FDR procedure not named in the provided text
Statistical tests used
Test Applied to n Assumptions
Riborex differential translational-efficiency analysis (assessing whether RPF/ribosome-occupancy changes exceed mRNA-level changes) genome-wide identification of translationally activated (445) / repressed (597) genes upon Xrn1 knock-down (Fig. 5b) n = 2 biological replicates per condition (RPF and RNAseq libraries) not stated
DESeq2 (log2 fold-change estimation for mRNA and RPF levels) computing log2FC used to classify gene groups (Fig. 5b) n = 2 biological replicates per condition not stated
Gene ontology enrichment / GSEA (Biological Process and Cellular Component), with Revigo redundancy removal functional enrichment of translationally regulated gene groups (Fig. 5c) na
Principal-component analysis (clustering of replicates) quality assessment of RPF and RNAseq libraries (Supplementary Fig. 9) n = 2 per condition na
Approaches that could also have been used
  • Genome-wide differential translational-efficiency testing was performed with n = 2 biological replicates per condition.
    Could also: Including additional biological replicates (e.g., n = 3 or more) per condition would also be a standard choice. — More replicates would increase the precision of dispersion estimates used by DESeq2/Riborex and is often preferred for stabilizing variance estimation in count-based RNA/RPF data.
  • Low-throughput quantifications were summarized as mean ± SEM from n = 3.
    Could also: Reporting the standard deviation or a 95% confidence interval alongside the individual points would also convey the data spread. — SD or a CI communicates the variability of the observations themselves rather than the precision of the mean, which many style guides favor for small n; the authors' practice of overlaying individual data points already supports this transparency.
  • The FDR threshold (FDR < 0.05) was used to call significance for genome-wide hits, with the specific procedure not named in the provided text.
    Could also: Explicitly stating the multiple-testing method (e.g., Benjamini-Hochberg) and reporting adjusted p-values per gene would also be a common approach. — Naming the correction and providing per-gene adjusted values aids reproducibility and lets readers evaluate the family-wise/false-discovery control applied.
  • Group comparisons (e.g., WT vs xrn1∆ for blot/luciferase quantifications) are presented with descriptive means and SEM.
    Could also: Pairing these descriptive summaries with a stated inferential test (e.g., a t-test or Mann-Whitney U for small n) and reported effect sizes would also be an option. — An explicitly named test with exact p-values and effect sizes would let readers gauge both statistical and biological magnitude for each comparison.
  • GO/GSEA enrichment terms were reported ordered by p-value after Revigo redundancy removal.
    Could also: Reporting the enrichment adjusted p-values (and the background gene set used) would also be standard. — Documenting the background universe and the multiplicity adjustment for the enrichment family helps readers interpret and reproduce the functional-enrichment results.
  • Randomization and blinding were not described.
    Could also: Stating whether sample processing/quantification was blinded or the order randomized would also be a common reporting practice. — Explicit statements about blinding/randomization (even when 'not applicable') improve transparency for quantitative image- and assay-based measurements.
Software: Riborex (R package) · DESeq2 · Revigo · GSEA / GO enrichment tool

Result convergence & founder nodes

Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.

Citation network

Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.

Citations
71
Impact: high
Foundation confidence
None of its references are in our reproducibility record yet — its foundation cannot be assessed.
Topics

No assessed neighbours yet — the network grows as more papers are assessed.

Data lineage

The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.

3FQD PDBe in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
3PIF PDBe in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet

What was reproduced

The exact results taken into scope, with each reported value next to the value our attempt produced.

Scope — pmid-30899024 (Blasco-Moreno et al. 2019, Nat Commun)

"The exonuclease Xrn1 activates transcription and translation of mRNAs encoding membrane proteins."

Code/data availability (verbatim findings)

  • Data availability (paper): "Ribosome Profiling and Genomic Run-On (GRO) raw data are available under accession GSE109734 and GSE123326 at Gene Expression Omnibus (GEO)."
  • Code availability (paper): NONE present. No GitHub URL in the paper.
  • Harvested code link github.com/abelew/prfdb is a MIS-HARVEST: it is a 2009–2016 Perl tool ("scripts to search genomic data for significant secondary structures", prfdb = Programmed Ribosomal Frameshift DB, Belew/Dinman lab) — unrelated to this Xrn1 transcription/translation paper. Do NOT use it. Reproduction proceeds per P16: apply the documented third-party pipeline to the paper's own data.

Pipeline (from Methods, well-specified)

  • Aligner: Bowtie -S -t -p 30 -n 1 -m 1 -l 25 --norc to sacCer3 transcriptome (SGD).
  • Ribo-seq: footprints filtered to 28–32 nt.
  • Differential translation: Riborex R-package v1.2.3 (FDR < 0.05) — the core tool.
  • DESeq2 for moderated log2 fold changes.
  • GO: gProfileR (FDR, moderate hierarchical filtering) + REViGO.

IN SCOPE (pipeline-derived, attempted)

  • R1 (headline): # genes translationally activated by Xrn1 = 445 (FDR<0.05);

    translationally repressed = 597 (FDR<0.05). Reproduce by running Riborex on the

    GEO processed count tables (GSE109734_raw_counts_RPF.tsv.gz, GSE109734_raw_counts_mRNA.tsv.gz) with the paper's contrast. 80/20 PRIMARY TARGET.

IN SCOPE (secondary, if time)

  • R2: 2.09-fold enrichment of membrane proteins among activated transcripts (GO/gProfileR).
  • R3: 5'UTR length 80 nt (activated) vs 52 nt (background).

OUT OF SCOPE (wet-lab / not pipeline / external)

  • Auxin-degron western blots, GRO transcription-rate wet measurements, polysome profiles, microscopy/ER-localization imaging, half-life (mRNA stability ±0.433 log2FC) is borderline — derived from decay measurements, attempt only if a count/decay table is shipped.

Data

  • GSE109734: yeast S. cerevisiae, Ribo-seq + RNA-seq, auxin-inducible Xrn1 degron, t0 vs t30 (30 min auxin), WT + Xrn1-D208A strains, 20 GSM samples. SRA: SRP131527.
  • Processed count tables shipped as series supplementary → enables count-based repro.
R1a
Reported
445 genes translationally activated by Xrn1 (Riborex FDR<0.05)
Reproduced
429 (auxin-degron t0->t30 contrast)
within tolerance
R1b
Reported
597 genes translationally repressed by Xrn1 (Riborex FDR<0.05)
Reproduced
585 (auxin-degron t0->t30 contrast)
within tolerance

Assessments & scoring basis

Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.

🤖 AI curator · claude (ai-curator room) · v1.0 L1 85/100

An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.

🟢1. Data identity
🟢2. Endpoint comparability
🟡3. Location of the main deviation
🟢4. Cause of the deviation
🟢5. Derivability / plausibility
🟢6. Severity of the deviation
🟢7. Core claim
🟡8. Severity of the miss (overall human judgment)
Scoring basis — itemised

Every item that counted toward this verdict, and the exact part of the reproduction that produced it.

Supporting (toward a concern)
Content-critical question only partially held
+2 pts
From: Q8 · Severity of the miss (overall human judgment) 🟡
Minor / cosmetic deviation
+1 pts
From: Q3 · Location of the main deviation 🟡
Concordant (toward reproduced)
Code + data deposited & functional
-2 pts
From: Data & code availability Available & functional
Total score +1

The headline claims reproduce cleanly: 445→429 (−3.6%) activated and 597→585 (−2.0%) repressed genes on the correct auxin-degron t0→t30 contrast, derivable from the public GEO GSE109734 count tables with the documented Riborex pipeline — no fabrication signal. The small gap sits on the input/preprocessing side (unspecified gene-filtering and DESeq2 independent-filtering defaults), and we had to self-select the matching contrast (the alternative WT-vs-D208A gives 1030/1059). The registry code link is a mis-harvest, so the run used the documented third-party tool, not authors' code. Overall a solid, core-confirming reproduction with minor explainable deviations; secondary claims R2 (2.09× membrane enrichment) and R3 (5′UTR length) were out of scope and unchecked.

🤝
Reproduced automatically — and fairly

Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.

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Reproduction footprint

claude-opus-4-8

Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.

252 k
tokens (I/O) · 28.4 M incl. cache
36 min
runtime · 0.02 CPU-h
1.9 GB
peak RAM
2 (1 failed)
HPC jobs
hummel
machine