Mitochondrial volume fraction and translation duration impact mitochondrial mRNA localization and protein synthesis.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡The central claim did not (fully) hold under reproduction
- 🟡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
Described well enough for the DEPOSITED-DATA layer, NOT for the core imaging pipeline. Reproduced 1:1: the GSE74454 (Couvillion 2016) ribosome-profiling columns that feed Figure 5 are exactly recomputable from the GEO deposit — 'Ribosome reads log2' and 'TE log2' are byte-exact (r=1.0, delta=0, n~5650), 'mRNA log2' is a perfect linear match up to a constant 0.74-log2 normalization offset (RPKS vs RPKM). Figure 5E's '>2-fold more ribosomes for ATP3-type' reproduces (ATP3-type 2.85x, TIM50-type 1.86x, p=1.3e-4) under inferred group definitions. The MLR class counts and the cited 130/551 (Williams 2014) reproduce closely (140/578). NOT attempted / NOT reproducible: the paper's CORE result — 3D imaging quantification of mRNA-to-mitochondria distance via the Mitograph_Distance repo (Analyze_Distance / Random_Walk_Distance, ParaView/Python2) — because its inputs (TrackMate foci coordinate tables + MitoGraph VTK surfaces from live-cell confocal microscopy) are NOT deposited anywhere and the repo ships only the 2 scripts with no example data; also out of scope: the analytic Brownian/equilibrium-binding model and all wet-lab/imaging measurements. No fabrication detected in any value checkable from shipped data. Verdict: PARTIAL — the deposited ribosome-profiling re-analysis reproduces exactly; the imaging core is unreproducible due to data unavailability (data_unavailable for that component only).
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
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
-
v1 current initial assessment Score 81assessed: 2026-06-18 ⛓ 3ad87a0a229e
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
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
- 2026-06-18
- Rubric version
- v1.0
- Assessed by
-
🤖 AI curator · claude (ai-curator headless) · v1.0 · run #1 2026-06-18no 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: opusDoes the metabolic state of yeast cells, specifically the change in mitochondrial volume fraction and the kinetics of protein synthesis, regulate condition-dependent localization of nuclear-encoded mRNAs to the mitochondrial surface and thereby control protein synthesis required for respiratory growth?
- ★ mRNA localization to mitochondria is condition-dependent: ATP3 mRNA switches from low (diffuse) association in fermentative conditions to strong mitochondrial association in respiratory conditions, while TIM50 is constitutively localized and TOM22 is diffuse. finding
- ★ Increased mitochondrial volume fraction during respiratory growth drives increased localization of nuclear-encoded mRNAs to the mitochondrial surface. mechanism
- ★ Mitochondrial mRNA localization is necessary and sufficient to increase protein production to levels required during respiratory growth. finding
- ★ Ribosome stalling/translation elongation kinetics affect mRNA sensitivity to mitochondrial volume fraction and counterintuitively enhance protein synthesis by increasing mRNA localization to mitochondria. mechanism
- ★ Live-cell methodology combining Su9-mCherry mitochondrial matrix marker, MS2-MCP single-molecule mRNA imaging, and MitoGraph V2.0 3D reconstruction quantifies mRNA-mitochondria spatial relationships. method
- ★ TOM22 mRNA shows a linear increase in mitochondrial co-localization directly proportional to mitochondrial volume fraction; ATP3 is more sensitive to volume fraction than TIM50 and TOM22. finding
- Mathematical modeling and in silico Brownian/thermodynamic-equilibrium experiments predict a stoichiometric correlation between mitochondrial volume fraction and mRNA localization. method
- ★ Perturbations raising mitochondrial volume fraction (sch9Δ, reg1Δ mutants, chloramphenicol) increase ATP3 mRNA localization to mitochondria even in glucose conditions. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Live-cell single-molecule mRNA and mitochondrial 3D imaging (MS2-MCP tethering + Su9-mCherry matrix marker, microfluidics, Z-stack time-lapse) | S. cerevisiae (yeast) | none (fermentative vs respiratory growth conditions) | proportion of mitochondria-associated mRNA per cell; mRNA-mitochondria distance; mitochondrial volume fraction | MS2-MCP system, Su9-mCherry; analysis via ImageJ Trackmate and MitoGraph V2.0 |
| Single-molecule FISH (validation) | S. cerevisiae (TOM22-tagged cells ± CYC1p-MS2-CP-GFP) | none | co-localization ratio of MS2-tag (Cy3) with ORF (Cy5) foci; foci number vs live imaging | Cy5/Cy3 fluorophore-conjugated 20nt FISH probes |
| Single-cell mRNA quantification | S. cerevisiae | fermentative vs respiratory | mRNA molecules per cell (MCP-GFP foci count) for ATP3 and TIM50 | — |
| Single-cell protein quantification (GFP fluorescence) | S. cerevisiae | fermentative vs respiratory | Atp3p-GFP and Tim50p-GFP fusion protein fluorescence intensity per cell | — |
| Western blot | S. cerevisiae | fermentative vs respiratory | Atp3p-GFP and Tim50p-GFP fusion protein levels | anti-GFP antibody |
| Live-cell imaging with genetic/chemical perturbation | S. cerevisiae (WT, sch9Δ, reg1Δ mutants) | sch9Δ KO, reg1Δ KO, chloramphenicol addition (1 µg/ml) | ATP3 mRNA mitochondrial localization, mitochondrial volume fraction, cell volume, mitochondrial volume, vacuole volume fraction | Su9-mCherry |
| RT-qPCR | S. cerevisiae (fermentative/respiratory; WT and mutant strains) | fermentative vs respiratory; mutant strains | OM14 and OM45 transcript levels | primers for OM14 and OM45 |
| In silico / mathematical modeling (Brownian particle distribution, thermodynamic binding equilibrium) | computational model based on measured cell and mitochondrial boundaries | none | predicted proportion of mRNA localization vs mitochondrial volume fraction | — |
- ▲ ATP3 protein levels increased in respiratory versus fermentative conditions 4-fold
- ▲ ATP3 mRNA levels increased less than protein in respiratory versus fermentative conditions less than 2-fold
- ▲ ATP3 mRNA shifts from low mitochondrial association in fermentative conditions to strong mitochondrial surface association in respiratory conditions
- – TIM50 mRNA (constitutively localized) showed no change in protein or mRNA levels in respiratory conditions
- ▲ TOM22 mRNA co-localization increased linearly in direct proportion to mitochondrial volume fraction
- ▲ ATP3 mRNA localization was more sensitive to mitochondrial volume fraction than TIM50 and TOM22, independent of nutrients
- ▲ sch9Δ, reg1Δ mutants and chloramphenicol addition raised mitochondrial volume fraction and increased ATP3 mRNA localization in glucose conditions
- fold_change 4-fold (ATP3 protein level increase in respiratory vs fermentative conditions)
- fold_change less than 2-fold (ATP3 mRNA level increase in respiratory vs fermentative conditions)
- count n > 27 (cells per condition for mRNA localization/volume fraction measurements)
- other 2x mode = 0.19 µm (distance between mitochondria and TIM50 mRNA used as localization threshold)
- count TIM50 mRNA 582 foci; TOM22 mRNA 476 foci (number of foci analyzed for mRNA-mitochondria distance distribution)
- other equilibrium constants 2.4K0 and 8.8K0 (mathematical modeling equilibrium constants for mRNA-mitochondria binding)
- count N > 103 (manually counted co-localized ORF/MS2-tag foci in smFISH validation)
- pvalue p<0.0001; p<0.001; p<0.01 (Mann–Whitney U-test significance thresholds for mRNA/protein per-cell comparisons)
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.
This study uses live single-molecule fluorescence imaging and 3D mitochondrial reconstruction in S. cerevisiae to quantify mRNA localization to mitochondria across metabolic conditions, complemented by single-cell and bulk protein measurements. Group comparisons are made with the Mann-Whitney U-test; continuous relationships between mitochondrial volume fraction and mRNA localization are described with linear regression and a thermodynamic/mathematical model. Dispersion is reported as SEM for cell-population data and SD for replicate biochemical experiments.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Mann-Whitney U-test (two-sided, non-parametric) | mRNA counts per cell (Figure 1C) and protein fluorescence per cell (Figure 1D) across fermentative vs. respiratory conditions | n > 27 cells per condition | not stated |
| Mann-Whitney U-test (two-sided, non-parametric) | Validation of live-imaging foci counts vs. smFISH foci counts (Figure 1—figure supplement 1C) | N > 43 cells | not stated |
| Linear regression (ordinary least squares) | Relationship between mitochondrial volume fraction and proportion of mRNA localization per single cell (Figure 2B, 2D, 2—figure supplement 2C) | n > 27 cells per condition | not stated |
| Mathematical/thermodynamic equilibrium model (in silico particle simulation) | Predicted relationship between mitochondrial volume fraction and mRNA localization proportion (Figure 2C–E, 2—figure supplement 2) | null | not stated |
| RT-qPCR (quantification method, not an inferential test per se) | OM14 and OM45 transcript levels in fermentative vs. respiratory conditions and mutant strains (Figure 2—figure supplement 5) | three independent experiments | not stated |
-
Dispersion around means is reported as SEM for single-cell proportion data (n > 27)↳ Could also: Report SD or 95% CI alongside or instead of SEM — SEM shrinks with larger n and reflects precision of the mean estimate rather than spread of the data; SD or 95% CI more directly conveys biological variability across cells, which is often the quantity of interest when characterising cell-to-cell heterogeneity in localization
-
Multiple pairwise Mann-Whitney U-tests are used across conditions and mRNA species without a stated multiplicity correction↳ Could also: Apply a family-wise correction (e.g., Bonferroni, Holm) or FDR correction (e.g., Benjamini-Hochberg) across the set of related comparisons — When several comparisons are drawn from the same experiment, a correction controls the probability that any single comparison appears significant by chance; this is commonly expected by reviewers and standard in multi-group cell biology studies
-
Pairwise comparisons between conditions use the Mann-Whitney U-test independently for each pair↳ Could also: Use a Kruskal-Wallis test followed by Dunn's post-hoc test (or one-way ANOVA with Tukey HSD if normality holds) when comparing more than two groups simultaneously — An omnibus test first establishes that at least one group differs before post-hoc comparisons, reducing the risk of inflated Type I error when multiple groups are compared
-
The relationship between mitochondrial volume fraction and mRNA localization proportion is characterised with ordinary linear regression↳ Could also: Use a generalised linear model (e.g., beta regression or logistic regression) given that the outcome is a proportion bounded between 0 and 1 — OLS linear regression can predict values outside [0,1] and may violate homoscedasticity near the boundaries; beta regression is designed for proportional outcomes and may provide better-calibrated confidence intervals and predictions
-
Effect sizes are described narratively ('4-fold increase') without a standardised metric↳ Could also: Report a standardised effect size such as rank-biserial correlation r for Mann-Whitney comparisons, or Cohen's d for continuous measurements — Standardised effect sizes allow readers to assess practical significance independently of sample size, and facilitate meta-analytic comparisons with future studies
-
Sample sizes (n > 27 cells, three biological replicates) are stated descriptively without a power analysis or justification↳ Could also: Include an a priori power calculation or cite a precedent from prior similar imaging studies to justify the chosen n — A stated power calculation clarifies what effect sizes the study was designed to detect, helping readers interpret both significant and non-significant results in context
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
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.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The deposited-data layer behind Figure 5 reproduces essentially 1:1 — C1/C2 are byte-exact (r=1.0, Δ=0) and C3 matches perfectly up to a benign RPKS-vs-RPKM normalization offset, confirming GSE74454 genuinely underlies the paper; Fig5E's '>2-fold' claim reproduces in direction, magnitude and significance (ATP3-type 2.85×, p=1.3e-4) under inferred cohort definitions. The central novelty — 3D imaging of mRNA-to-mitochondria distance — could not be reproduced because its microscopy-derived inputs were never deposited and the repo ships no example data; this is an authors'/data-availability gap (q1/q2), not a fabrication or computation defect. No fabrication was detected in any value checkable from shipped data, so overall this is a solid-but-partial reproduction: the verifiable portion is clean, the core remains untestable.
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.
Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.
🚩 Report an error in this record
Spotted something wrong — a verdict you’d contest, a data or value error, or a private detail that slipped through? Tell us, with a short justification. Authors and readers are equally welcome to write in; we review every report.
Prefer email, or the form below not working? Contact us at support@doesitreproduce.com.
Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.